Automatically suggesting completions of text

ABSTRACT

A user may respond to a request of another user by entering text, such as a customer service representative responding to a customer. As the responding user enters text, a completion of the text may be suggested to the responding user so that the responding user may select the completion instead of continuing to enter text. Previous messages between the two users and other information may be used to determine an appropriate completion to the entered text. A topic vector may be determined from previous messages, and a feature vector may be determined from the entered text. The topic vector and the feature vector may be used to identify characters that may follow the entered text.

CLAIM OF PRIORITY

This patent application claims the benefit of the following provisionalpatent application, which is hereby incorporated by reference in itsentirety: U.S. Patent Application Ser. No. 62/359,841, filed Jul. 8,2016 (ASAP-0001-P01).

FIELD OF THE INVENTION

The present invention relates to automating or assisting communicationsusing semantic processing.

BACKGROUND

Companies need to efficiently interact with customers to provideservices to their customers. For example, customers may need to obtaininformation about services of the company, may have a question aboutbilling, or may need technical support from the company. Companiesinteract with customers in a variety of different ways. Companies mayhave a website and the customer may navigate the website to performvarious actions. Companies may have an application (“app”) that runs ona user device, such as a smart phone or a tablet, that provides similarservices as a website. Companies may have a phone number that customerscan call to obtain information via interactive voice response or tospeak with a customer service representative. Companies may also respondto customers using various social media services, such as Facebook orTwitter.

Some existing techniques for allowing customers to interact withcompanies may be a nuisance to the customer. Navigating to the rightpage on a website or an app or navigating a voice menu on a phone callmay be time consuming. Some existing techniques for allowing customersto interact with companies may be expensive for a company to implement.Hiring customer service representatives to manually respond to requestsand answer phone calls may be a significant expense.

BRIEF DESCRIPTION OF THE FIGURES

The invention and the following detailed description of certainembodiments thereof may be understood by reference to the followingfigures:

FIGS. 1A and 1B illustrate an example user interface on a customerdevice.

FIG. 2 illustrates a system for using semantic processing to respond toa customer request.

FIG. 3 illustrates a system for automatically responding to a customerrequest using semantic processing.

FIG. 4 illustrates an action graph that may be used to determine anaction in response to a customer request.

FIG. 5 is a flowchart of an example implementation of automaticallyresponding to a customer request using semantic processing.

FIG. 6 is a flowchart of an example implementation of a greedy algorithmfor selecting a node.

FIG. 7 is a flowchart of an example implementation of a beam searchalgorithm for selecting a node.

FIG. 8 illustrates a search graph that may be used to select a node ofan action graph.

FIG. 9 illustrates a search graph that may be used to determine acompletion of text using a word-based language model.

FIG. 10 illustrates a system for determining completions of text using aclassifier.

FIG. 11 illustrates a search graph that may be used to determine acompletion of text using a character-based language model.

FIG. 12 is a flowchart of an example implementation of a determining acompletion of text using semantic processing.

FIG. 13 illustrates a system for determining suggested responses usingsemantic processing.

FIG. 14 illustrates a search graph that may be used to suggest aresponse.

FIG. 15 is a flowchart of an example implementation of a suggesting aresponse using semantic processing.

FIG. 16 illustrates a system for suggesting a resource using semanticprocessing.

FIG. 17 is a flowchart of an example implementation of a suggesting aresource using semantic processing.

FIGS. 18A-18Q illustrate example user interfaces for a customer supportsession between a customer and a customer support representative.

FIGS. 19A-19C illustrate systems for providing a semantic processingservice.

FIG. 20 is a flowchart of an example implementation of providing asemantic processing service.

FIG. 21 is an exemplary computing device that may be used to performsemantic processing.

DETAILED DESCRIPTION

Described herein are techniques for using semantic processing to respondto a request of a user. Although the techniques described herein may beused for a wide variety of users and requests, for clarity ofpresentation, an example of a company providing a response to a requestof a customer will be used. The techniques described herein, however,are not limited to customers and companies, responses may be provided torequests from users who are not customers, and responses may be from anentity that is not a company, such as an individual. Semantic processingmay be used to automate responses to a request and to assist aresponding user in formulating a response to a request.

Semantic processing may be used to provide a fully automated experiencefor the customer. The use of semantic processing allows a customer toobtain needed information or needed support by making a request usingnatural language. The use of natural language for making requests, mayallow the customer to receive a resolution to his request more quicklythan with other methods of making requests. In some instances, thecustomer's request may be resolved with a single interaction. Forexample, where the customer asks “When is my package arriving?,” theresponse may be, “Your package is scheduled to arrive on Wednesday andhas tracking number 1234.”

FIGS. 1A and 1B illustrate an example user interface 100 on a customerdevice that may be used for providing automatic responses. FIG. 1Aillustrates an example of a text box 110 presented on a customer devicewhere a customer can send a request to a company. The customer may typethe request using natural language in the same manner as if the customerwas sending the message to a person. The customer may submit the requestusing a button, such as button 120. In some implementations, thecustomer may speak the request and the speech may be converted to texton the customer device or at a server computer.

After the customer submits the request, the request may be automaticallyprocessed. For example, semantic processing techniques may be used tounderstand the request and provide a response. FIG. 1B illustrates threeexample responses to the customer's request. In user interface 130, aresponse is provided that immediately answers the customer's question.In user interface 140, the automatic processing has determined that moreinformation is needed from the customer to provide a response, and thecustomer is asked to provide additional information. In user interface140, the customer is asked to select one of several possible options butin other instances or implementations, the customer may be asked toprovide additional information in other ways, such as by clarifying theresponse by typing additional text. In user interface 150, the automaticprocessing has determined to connect the customer with a customerservice representative. The techniques described herein are not limitedto the foregoing example responses, and any suitable response may beprovided to a customer. For example, a customer requesting to change hisor her phone number may be immediately provided with a form to allow thecustomer to enter a new phone number (e.g., by automatically navigatingthe app to the appropriate screen or providing an appropriate web page).

In addition to providing automatic responses, semantic processing mayalso be used to assist a customer service representative (CSR) inresponding to a request of a user. For instance, in a conversationbetween a customer and a CSR, semantic processing may be used tounderstand the meaning of a customer request and to provide suggestionsto a CSR, such as when a CSR starts typing a response, completions tothe typed text may be suggested; before a CSR starts typing, completeresponses may be suggested to the CSR; resources may be suggested to theCSR to provide the CSR with relevant information or to make it easierfor the CSR to perform actions; and the like.

Semantic Processing System

FIG. 2 illustrates a system 200 for using semantic processing to respondto a customer request. In FIG. 2, a customer may use customer device 210to communicate with a company. Customer device 210 may include anyappropriate device, such as a smart phone, tablet, wearable device, orInternet of things device. The customer may submit the request using anyappropriate technique, such as typing or speaking a request to an apprunning on customer device 210 (e.g., an app of a particular company ora third-party app created for processing customer requests), typing orspeaking a request on a web page, sending a text message, or sending anemail. As used herein, a text message includes any message sent as textincluding but not limited to a message sent using SMS (short messageservice) or a special purpose application (e.g., Facebook messenger,Apple iMessage, Google Hangouts, or WhatsApp).

The customer's request may be sent by customer device 210 to applicationinterface component 220, and may be sent either directly or indirectlyusing any appropriate network (e.g., Internet, Wi-Fi, or a cellular datanetwork). The request may be sent using any appropriate transmissionprotocols that include sending one or more of the text of the message oraudio of the customer speaking the request. Where the customer speaks arequest to customer device 210, speech recognition may be performed bycustomer device 210, at a server computer, or by another component ofFIG. 2. Application interface component 220 may include, for example,one or more server computers operated by a company.

Application interface component 220 receives the customer request andmay coordinate further processing of the customer request. Where thecustomer request is to be processed automatically, the customer requestmay be routed to semantic response component 240 to determine a responsewithout the involvement of a CSR. Semantic response component 240 mayperform semantic processing on the text of the customer request tounderstand the meaning of the request, select an action to perform inresponse to the request, and cause a response to be presented to thecustomer. In addition, semantic response component 240 may provideresults to application interface component 220 for use by customersupport component 230.

Where the customer request from customer device 210 is to be sent to aCSR, the customer request may be routed to customer support component230 to direct the request to a CSR and obtain a response from the CSR.Customer support component 230 may be connected with multiple CSRs, suchas CSR 251, CSR 252, and CSR 253. Each CSR may use a user interface,such as an application on a computer or a web page, to receive customerrequests and provide responses to them.

Customer support component 230 may include other components to assistthe CSRs, such as auto-complete component 231, auto-suggest responsescomponent 232, and auto-suggest resources component 233. These threecomponents may perform semantic processing on the text of messagesbetween the customer and the CSR to understand the meaning of theconversations, and provide suggestions to the CSR to assist the CSR inresponding to a customer. Auto-complete component 231 may assist a CSRby providing suggested completions to text that the CSR has startedtyping, auto-suggest resources component 232 may suggest completeresponses to a CSR before a CSR has started typing, and auto-suggestresources component 233 may suggest resources to the CSR to provide theCSR with relevant information or allow the CSR to take appropriateactions.

System 200 is one example implementation of using semantic processing torespond to a customer request, but the techniques described herein mayuse any appropriate implementation and are not limited to the example ofFIG. 2. For example, the components of FIG. 2 may be split into multiplecomponents or merged together, some processing may be performed bycustomer device 210, and other components may be included, such as loadbalancing or any other components that may be used with a networkservice. In addition, some components of system 200 may be implementedby a company whose products and services are being supported, and othercomponents may be provided by a third-party. For example, the companymay provide customer support component 230 in association with CSRs 251,252, and 253, but may have the semantic response component 240functionality provided by a third-party.

Automated Responses

FIG. 3 illustrates a system 300 for implementing semantic responsecomponent 240. System 300 may receive the customer request (e.g., in theform of text) from application interface component 220 and performautomated semantic processing on the request using one or more of NLP(natural language processing) feature extraction component 310, nodeselector component 320, and action selector component 330, which aredescribed in greater detail below.

NLP feature extraction component 310 may compute features from the textto be used in subsequent processing. The features may include anyappropriate features that are used for natural language processing.Operations that may be performed by NLP feature extraction component 310may include one or more of preprocessing, encoding, and featureextraction.

Preprocessing in NLP feature extraction component 310 may include anyappropriate preprocessing tasks. For example, the text may be convertedto lower case, stripped of punctuation, and tokenized. In someimplementations, less common words (e.g., words not on an allowed wordslist) may be replaced by an unknown token (e.g., “UNK”) indicating thatthe word is not on the allowed words list.

After preprocessing, the text may be encoded. Any appropriate encodingtechniques may be used. In some implementations, the text may bevectorized into a bag of words (BOW) vector, a term frequency inversedocument frequency (TFIDF) vector, or a matrix of word embeddings (e.g.,as obtained using a Word2Vec model or a GloVE algorithm).

After encoding, features may be extracted or computed. Any appropriatefeatures may be used. In some implementations, where the text wasencoded with BOW or TFIDF, the features may be extracted by processingthe encoded text with a topic model, such as a latent semantic indexingmodel or a latent Dirichlet allocation model, or by processing theencoded text with a neural network, such as a multi-layer perceptronneural network. In some implementations, where the text is encoded usingword embeddings, features may be obtained by processing the matrix witha neural network, such as a deep averaging network, a convolution neuralnetwork, a recurrent neural network, a recursive neural network, or anyother suitable type of neural network.

The features may be processed to determine an appropriate action to takein response to the customer's request. In some implementations, thepossible actions may be organized using an action graph, where a graphis a number of nodes connected by edges or links. For example, theaction graph may be a directed acyclic graph or a tree.

As used herein, a graph is a data structure or a data type that may beused in implementing an algorithm on a computer. A graph comprises nodesor vertices that may be connected by edges or arcs. The nodes and/oredges of a graph may be associated with other data being processed. Insome implementations, edges may be directed, which allows a transitionbetween nodes in only one direction. Although graphs are presentedvisually herein, when used to implement an algorithm, the graphs neednot be presented visually, and data describing the graph (e.g., datadescribing nodes and edges) may be used in implementing an algorithm.

FIG. 4 illustrates an example action graph that may be used to determinean action in response to a customer request. Although the action graphof FIG. 4 is illustrated as a tree, the action graph need not be a treeand may be another type of graph. In the example of FIG. 4, the top nodeis a Root node and all other nodes may be connected either directly orindirectly to the Root node. Underneath the Root node are three nodeslabeled Repair, Account/Billing, and Login/Password. Each of these threenodes may be used to determine an action to respond to a request of thecorresponding type.

For example, the Repair node, is connected to three further nodeslabeled Phone, Internet, and TV. Each of the Phone, Internet, and TVnodes may be associated with actions that may be used to provideresponses for those particular types of repairs. FIG. 4 further showsother examples of nodes that provide examples of types of actions thatare more specific than the nodes discussed above (e.g., the four nodesunderneath the Internet node). The dashed lines in FIG. 4 indicate edgeswhere subsequent nodes are possible but have been omitted for the sakeof clarity.

In some implementations, node selector component 320 may select a nodefrom the action graph using the NLP features. The selected node of theaction graph may be associated with one or more possible actions, andaction selector component 330, may then select an action from the one ormore actions associated with the selected node.

In some implementations, each node of the action graph may be associatedwith a node selector classifier, where a node selector classifier isconfigured to determine a score (e.g., a likelihood or probability)indicating a match between the node and the customer's request. Nodeselector component 320 may select a node using the node selectorclassifiers by computing scores using the node selector classifiers andthe NLP features. In some implementations, at least some nodes of theaction graph may also be associated with an action selector classifier,and action selector component 330 may select an action using an actionselector classifier. In some implementations, however, each node of theaction graph may be associated with a single action, and action selectorcomponent 330 and action selector classifiers may be omitted.

FIG. 5 is a flowchart of an example implementation of using semanticprocessing to determine an action to provide a response to a customer.In FIG. 5, the ordering of the steps is exemplary and other orders arepossible, not all steps are required and, in some implementations, somesteps may be omitted or other steps may be added. The process of theflowcharts may be implemented, for example, by any of the computers orsystems described herein.

At step 510, text of a customer request is obtained, and at step 520features are obtained from the customer text. Steps 510 and 520 may beimplemented using any of the techniques described herein, and step 520may be implemented, for example, by NLP feature extraction component310. For example, at step 510, an audio signal comprising speech of acustomer request may be processed using speech recognition to obtaintext of the customer request.

At step 530, a node of the action graph is selected using the features.For example, step 530 may be implemented by node selector component 320.In some implementations, each node of the action graph may be associatedwith a node selector classifier. A score may be generated for a node byprocessing the features with the node selector classifier that isassociated with the node. Scores may be generated for some or all of thenodes of the action graph, and a node may be selected using the scores.Some example implementations of selecting a node include (i) computing ascore for every node of the action graph and selecting a node with ahighest score; (ii) implementing a greedy algorithm that starts at theroot of the action graph, selects a child node having a highest score,and subsequently selecting a next child node with a highest score untila termination criteria is reached (e.g., reaching a leaf of the actiongraph); or (iii) implementing a beam search in traversing the actiongraph.

The node selector classifier may be any appropriate classifier, and nodeselector classifiers may be retrieved from a data store, such as nodeclassifiers data store 341. In some implementations, the node selectorclassifier may include a logistic regression classifier (such as astructured multinomial logistic regression classifier), a support vectormachine, or a decision tree. Further details of example implementationsof selecting a node are described below.

Step 530 may be implemented using other data in addition to the NLPfeatures. For example, step 530 may be implemented using customer data,such as customer data retrieved from customer data data store 342 andother data, such as other data retrieved from other data data store 343.When receiving a request from the customer, the request may include acustomer identifier (such as a customer identification number, customeruser name, or device identifier) that may be used to obtain informationabout the customer from customer data data store 343. The customer datamay include any information about the customer, such as a customerprofile, a customer location, billing and payment data, and servicesprovided to the customer.

The customer data may be used by node selector component 320 to select anode to provide a response to a customer. For example, a customer may besubscribed to Internet services but may not be subscribed to phone or TVservices. When selecting a node to respond to the customer's request,the nodes of the action graph relating to phone and TV services may bepenalized in computing a score or may not be considered at all.

Other data may also be used by node selector component 320 in selectinga node. In some implementations, the company may have a knowledge basethat contains relevant information for responding to a customer request.For example, the company knowledge base may include information aboutcurrent or recent service outages, and this information may be used inselecting a node. When a customer asks for help with the Internet notworking, the customer's address can be compared with locations ofcurrent service outages. If a service outage is affecting the customer,a node may be selected relating to providing a response with informationabout the service outage, such as an expected resolution of the outage.If the customer is not affected by any outages, then a node may beselected to help the customer troubleshoot why his or her Internet isnot working.

The customer data and other data may be combined with the NLP featuresin selecting a node. In some implementations, a feature vector of NLPfeatures may be augmented with additional features generated from thecustomer data or the other data. The features relating the customer dataand other data may be in any appropriate format. For example, a featuremay be 0 or 1 to indicate a state, such as whether there is an outage orwhether there is a past due amount for the customer's bill. Features maytake integer values, floating point values, or may be one a specifiedset of values (e.g., services provided to the customer may be set tosome combination of the strings “Internet,” “Phone,” and “TV”). Thecombined feature vector may be input into the node selector classifiersto generate scores that may be used in selecting a node.

At step 540, an action is selected using the selected node. For example,step 540 may be implemented by action selector component 330. Where theselected node is associated with only a single action, that action maybe selected. Where the selected node is associated with multipleactions, an action selector classifier may be used to select an action.Action selector component 330 may also use customer data from customerdata store 342 and other data from other data data store 343 inselecting an action for the selected node. For example, a selected nodemay have a multiple actions available depending on the customer data orthe other data.

At step 550, one or more components of system 200 may cause the actionto be performed. For example, application interface component 220 mayperform steps that cause a response to be presented to the customer,such as the responses of FIG. 1B. Additional details of performing anaction to provide a response to a customer are described below.

Further details of example implementations of node selector component320 are now presented. Node selector component 320 may implement asearch algorithm in selecting a node. For example, node selectorcomponent 330 may implement a greedy search, a beam search, or a Viterbisearch.

FIG. 6 is a flowchart of an example implementation of a greedy searchfor selecting a node of the action graph. In FIG. 6, the ordering of thesteps is exemplary and other orders are possible, not all steps arerequired and, in some implementations, some steps may be omitted orother steps may be added. The process of the flowcharts may beimplemented, for example, by any of the computers or systems describedherein.

At step 610, features are received. The features may include NLPfeatures and/or features obtained from customer data or other data.

At step 620, the root of the action graph is set as a “current node.”The current node may be a node that is being processed during aniteration of an algorithm. For example, the current node may bespecified by a node ID or a pointer. The root node of the action graphmay be any node that is considered to be a starting point of a search.In some implementations, the root node may only have outbound edges andmay not have any inbound edges.

At step 630, a score is computed for each child node of the currentnode. In some implementations, a score may also be computed for thecurrent node. The scores may be computed using any of the techniquesdescribed herein. In some implementations, each node may be associatedwith a node selector classifier, and the node selector classifierassociated with the node may be configured to compute scores for eachchild of the current node and optionally a score for the current node.

In some implementations, the node selector classifier may be a logisticregression classifier. For a node n, the logistic regression classifier,for instance, may have as parameters, a matrix W_(n) and a vector b_(n).Where there are N features (e.g., NLP features or features from customerdata or other data) and M child nodes of the current node, the matrixW_(n) may have M+1 rows (one for the current node and each child node)and N columns and the vector b_(n) may have length N. The classifier maycompute scores using the following equationp(c _(i) |x)=SoftMax(W _(n) x+b _(n))_(i)where x is a feature vector, c_(i) represents a node (e.g., a child ofthe current node or the current node), and the subscript i indicates thei^(th) element of a vector. For example, c₀ may represent the currentnode, c₁ may represent the first child of the current node, and c_(M)may represent the last child of the current node. The function SoftMaxmay be computed as follows:

${{SoftMax}(y)}_{i} = \frac{e^{y_{i}}}{\sum\limits_{j = 0}^{M}e^{y_{i}}}$

In some implementations, the node selector classifier may include anensemble of classifiers. For example, the node selector classifier maybe implemented using bootstrap aggregating or bagging or may beimplemented using stacking or feature weighted stacking.

At step 640, the child node with the highest score may be selected, andat step 650, the score of the highest scoring child may be compared to athreshold. If the score of the selected child node is above thethreshold, then processing may proceed to step 655 where it isdetermined if the selected child node is a leaf node (e.g., does nothave edges directed to another node). If the selected child node is aleaf node, then processing may proceed to step 665 where the selectedchild node is used for further processing, such as selecting an actionusing an action selector model.

If the selected child node is not a leaf node, then processing mayproceed to step 660, where the selected child node is set as the currentnode. After step 660, processing may proceed back to step 630 wherescores may be computed for the children of the new current node. Byiterating in this manner, the action graph may be traversed byrepeatedly traversing to a best-scoring child node. The algorithm may bereferred to as a greedy algorithm because each step of the processingchooses a highest-scoring child as the next step.

If the score of the selected child node is not above the threshold, thenprocessing proceeds to step 670, where a score for the current node iscompared to a threshold (which may be the same as or different from thethreshold of step 650). If the score of the current node exceeds thethreshold, then processing proceeds to step 680 where the current nodeis selected. The selected current node may then be used for furtherprocessing, such as selecting an action using an action selector model.

If the score for the current node is below the threshold, thenprocessing may proceed to step 690 where additional information isrequested from the customer. Where all the child nodes and the currentnode have scores below the threshold (or respective thresholds) then thelow scores may indicate an uncertainty as to the correct node forresponding to the customer's request. Instead of selecting a node (whichmay be an incorrect node), it may provide a better experience for thecustomer to obtain additional information and to have greater certaintyin finding an appropriate node to respond to the customer's request. Forexample, the customer may be asked to select one of several possiblechoices or to provide additional text to clarify his or her request.

Many variations of the steps of FIG. 6 are possible. For example, insome implementations, the current node at step 670 may always beselected even if the score for the current node is under the threshold.In such an implementation, steps 670 and 690 may be omitted, and the“no” branch of step 650 may connect to step 680.

FIG. 7 is a flowchart of an example implementation of a beam search forselecting a node of the action graph. In FIG. 7, the ordering of thesteps is exemplary and other orders are possible, not all steps arerequired and, in some implementations, some steps may be omitted orother steps may be added. The process of the flowcharts may beimplemented, for example, by any of the computers or systems describedherein.

At step 710, features are received. The features may include NLPfeatures and/or features obtained from customer data or other data.

At step 720, a list of active paths of a search graph is initialized,and the number of active paths may be referred to as a beam width. Thelist of active paths may be initialized with the root node as an activepath consisting of just the root node. During subsequent steps, theexisting active paths may be extended and other active paths may beadded up to the specified beam width. A number of active paths may be afixed number or may include all paths with a score exceeding athreshold. Each active path may be initialized with a path score that isupdated during subsequent steps.

At step 730, scores are computed for child nodes for the ends of all ofthe active paths. Scores may be computed using any of the techniquesdescribed herein. The first time step 730 is performed, the only activepath may correspond to the root node, and thus scores may be computedonly for children of the root node. For subsequent instances ofperforming step 730, there may be multiple active paths, and scores maybe computed for the child nodes of all active paths.

At step 740, path scores are computed for extending each of the activepaths with the child nodes of each active path. The path score of anextended path may be the product of the existing path score with thescore for the child node (or in some implementations may be the sum orsome other combination of the path score with the score for the childnode). Since each active path may have multiple child nodes, the numberof extended path scores computed at step 740 may be larger than the beamwidth.

At step 750, a number of top scoring extended paths corresponding to thebeam width are retained and other paths are discarded. The kept extendedpaths are the new set of active paths for subsequent processing. Forexample, a top scoring number of paths or all paths with a path scoreabove a threshold may be kept. In some implementations, a path may beretained as an active path without the addition a child node, and thismay be indicated by a special “stop” node in the search graph. Forexample, a path of length two may have a higher score than many paths oflength three, and thus the shorter path may be retained (path scores mayalso be normalized to account for different lengths).

At step 760, it is determined whether processing has completed. Anyappropriate criteria may be used to determine whether processing hascompleted. For example, processing may be completed when all of theactive paths (i) have reached a leaf node or (ii) do not have any childnodes with a sufficiently high score. If processing has not completed,then processing returns to step 730 where scores are computed for childnodes of the new set of active paths. If processing has completed, thena best active path is selected at step 770 and the node at the end ofthis path may be selected for determining an action to respond to thecustomer request.

FIG. 8 illustrates a search graph generated by an example beam search.In the example of FIG. 8, the beam width is 3 (the 3 top scoring pathsare retained), and at the end of the search, the three top scoring pathsare (1) Repair, Internet, Stop; (2) Repair Internet, Email; and (3)Login/Password, Login, Create. The active paths (nodes and edges) at theend of the search are indicated with solid lines and non-active pathsare indicated with dashed lines (a non-active path at the end of asearch may have been an active path during an earlier stage of thesearch). Note that the first selected path terminated before reaching aleaf node because the Repair, Internet path had a higher score than theunselected paths of length three. The termination of the Repair,Internet path may be indicated by the special Stop node that follows theInternet node.

Other search techniques may be used in addition to the search techniquesdescribed above. For example, a Viterbi search may be performed where ascore is computed along every possible path using a depth-first search.The node at the end of the highest scoring path may be selected.

Further details of example implementations of action selector component330 are now presented. In some implementations, each node is associatedwith a single action, and action selector component 330 may be omitted.In some implementations, each node will be associated with multipleactions, and action selector component 330 may be needed to select anaction regardless of which node is selected. In some implementations,some nodes may be associated with a single action and some nodes may beassociated with multiple actions, and action selector component 330 mayonly be needed when a node with more than one action is selected.

Action selector component 330 may use an action selector classifier toselect an action from a list of possible actions and may retrieve actionselector classifiers from a data store, such as action classifier datastore 344. The action selector classifier may be specific to aparticular node. Any suitable classifier may be used to select anaction. For example, an action selector classifier may include alogistic regression classifier, a support vector machine, or a decisiontree.

An action that is available at a node may include any sequence of eventsthat may be used to provide a response to a customer. For example,actions may include any of the following: (i) making a query to adatabase and providing the customer with the results of the query (e.g.,the customer asks when a package will be delivered, the deliveryinformation is retrieved from a database, and the delivery informationis presented to the customer); (ii) directing the customer to a page ofan app or of a website (e.g., a customer asks to change his password,and the app is directed to the appropriate page of the app or thebrowser is redirected to an appropriate web page); (iii) asking thecustomer to provide additional information about his or her request(e.g., the customer states that the Internet is not working and askingthe customer if the problem is with Wi-Fi, email, etc.); or (iv)connecting the customer with a CSR (e.g., starting a chat session or aphone call between the customer and a CSR). Where a company has CSRswith different specialties (e.g., technical support and billing), theaction to connect the customer with a CSR may also include connectingthe customer to CSR with the specialty corresponding to the customer'srequest. For example, where the customer is having a problem with theInternet connection, the customer may be connected to a CSR whospecializes in technical support.

An action selector classifier may use any of the feature types describedabove in selecting an action. For example, an action selector classifiermay use NLP features, features obtained from customer data, or featuresobtained from other data (such as a company knowledge base). A featurevector may be created and the action selector classifier may process thefeature vector to generate a score for each possible action. In someimplementations, an action with a highest score may be selected.

The output of semantic response component 240 may be an action. In someimplementations or instances, the node selected by node selectorcomponent 320 may have only one action associated with it, and thisaction is output by semantic response component. In someimplementations, the node selected by node selector component 320 may beassociated with multiple actions, and action selector component 330 mayselect the action to be output by semantic response component. Theaction output by semantic response component may take any appropriateform. For example, action may include an identification numberspecifying the processing to be performed or may include structured dataspecifying the processing to be performed.

An example of the overall operation of semantic response component 240is now provided. A customer may send a request to a company stating that“My Internet connection is not working.” NLP feature extractioncomponent 310 may process the text to generate NLP features. Next, nodeselector component 320 may use the NLP features, and optionally otherfeatures as described above, to select a node of the action graph.Starting at the root of the action graph of FIG. 4, node selectorcomponent 320 may compute a score for each child of the root node usinga node selector classifier. Node selector component 320 may traverse theaction graph to similarly compute scores for subsequent child nodes. Forexample, the action graph may be traversed from the Root node to theRepair node to the Internet node and to the Connection node. Nodeselector component 320 may then select the Connection node (e.g.,because it is a best scoring node according to the search algorithm).The Connection node may have two actions associated with it. The firstaction may be to provide the customer with information to troubleshoothis or her Internet connection, and the second action may be to informthe customer that there is a service outage in the customer's area.Because the selected node has more than one action, action selectorcomponent 330 may select an action from the available actions using anaction selector classifier. The features input to the action selectorclassifier may include the location of current service outages and theaddress of the customer. The action selector classifier may output ascore for each of the two possible actions, and a highest scoring actionmay be selected. Semantic response component 240 may return the highestscoring action to another component, such as application interfacecomponent 220.

In some implementations, the only available action at each node of theaction graph may be to connect the customer with a CSR, and theoperation of the classifiers is to select an appropriate CSR to handlethe customer's request. For example, for a large company, the companymay have several teams of CSRs where each team is trained to handleparticular types of request. For example, for the action graph of FIG.4, a company may have different teams of CSRs where each team isresponsible for responding to requests relating to one or more of thenodes. In these implementations, the only action at each node may be toconnect the customer with a CSR with the difference being which CSR teamthe customer will be connected to.

Other components of system 200, such as application interface component220 may cause the action to be performed and ultimately cause a responseto be presented by customer device 210 to the customer, such as any ofthe responses in FIG. 1B. In causing the action to be performed,application interface component 220 may interface with other componentsto obtain needed information and causing a response to be displayed tothe customer. For example, application interface component 220 mayperform one or more of the following steps: obtain a template (e.g., anHTML template) to present information on a display of customer device210; obtain text-to-speech audio to be played by a speaker of customerdevice 210; retrieve information from a data store using the action; orobtain the results of other processing by making a request to anothercomponent (e.g., using a REST interface to determine information aboutan outage at the customer's address); or transmit a request to acustomer service representative (such as CSR 251, 252, or 253) for theCSR to contact the customer.

Application interface component 220 may communicate with customer device210 using any appropriate techniques. For example, application interfacecomponent 220 may transmit any of the following to customer device 210:HTML to be presented by a display; audio to be played by a speaker (ortext to be used to generate audio at the customer device); a link to apage of an app or a website (e.g., a “deep link”).

Accordingly, a customer may submit a request to a company using naturallanguage, and receive an automatic response from the company. In someimplementations, a response may be provided to a user as described inthe following clauses, combinations of any two or more of them, or incombination with other clauses presented herein.

1. A computer-implemented method for automatically responding to arequest of a user, the method comprising:

receiving text corresponding to a user request;

computing a plurality of features from the text;

using a graph comprising a plurality of nodes, wherein:

-   -   the plurality of nodes comprises a first node and a second node,    -   the first node is associated with a first classifier and a first        action, and    -   the second node is associated with a second classifier and a        second action;

computing a first score for the first node using the first classifierand the plurality of features;

computing a second score for the second node using the second classifierand the plurality of features;

selecting the first node using the first score and the second score; and

causing the first action associated with the first node to be performed.

2. The computer-implemented method of clause 1, wherein the user is acustomer of a company and the user request seeks assistance from thecompany.

3. The computer-implemented method of clause 1, wherein causing thefirst action to be performed comprises transmitting information to theuser, requesting additional information from the user, or connecting theuser with a customer support representative.

4. The computer-implemented method of clause 1, wherein receiving textcorresponding to a user request comprises performing speech recognitionon an audio signal comprising speech of the user.

5. The computer-implemented method of clause 1, wherein the text isreceived via a text message, electronic mail, a web server, or anapplication running on a user device.

6. The computer-implemented method of clause 1, wherein selecting thefirst node comprises using a greedy search algorithm.

7. The computer-implemented method of clause 1, wherein computing thefirst score comprises using information about the user.

8. A system for automatically responding to a request of a user, thesystem comprising:

at least one server computer comprising at least one processor and atleast one memory, the at least one server computer configured to:

receive text corresponding to a user request;

compute a plurality of features from the text;

use a graph comprising a plurality of nodes, wherein:

-   -   the plurality of nodes comprises a first node and a second node,    -   the first node is associated with a first classifier and a first        action, and    -   the second node is associated with a second classifier and a        second action;

compute a first score for the first node using the first classifier andthe plurality of features;

compute a second score for the second node using the second classifierand the plurality of features;

select the first node using the first score and the second score; and

cause the first action associated with the first node to be performed.

9. The system of clause 8, wherein the first node is associated with afirst plurality of actions, the first plurality of actions comprisingthe first action; and wherein the at least one server computer isconfigured to select the first action from the first plurality ofactions.10. The system of clause 9, wherein the at least one server computer isconfigured to select the first action using information about the user.11. The system of clause 8, wherein the graph is a directed acyclicgraph.12. The system of clause 8, wherein the at least one server computer isconfigured to select the first node using a beam search algorithm.13. The system of clause 8, wherein the at least one server computer isconfigured to select the first node by:

setting a beginning node as a current node, the current node comprisinga plurality of child nodes;

computing a score for each child node of the plurality of child nodes togenerate a plurality of scores;

selecting a first child node of the plurality of child nodes using theplurality of scores; and

setting the first child node as the current node.

14. The system of clause 8, wherein the first classifier comprises alogistic regression classifier.

15. One or more non-transitory computer-readable media comprisingcomputer executable instructions that, when executed, cause at least oneprocessor to perform actions comprising:

receiving text corresponding to a user request;

computing a plurality of features from the text;

using a graph comprising a plurality of nodes, wherein:

-   -   the plurality of nodes comprises a first node and a second node,    -   the first node is associated with a first classifier and a first        action, and    -   the second node is associated with a second classifier and a        second action;

computing a first score for the first node using the first classifierand the plurality of features;

computing a second score for the second node using the second classifierand the plurality of features;

selecting the first node using the first score and the second score; and

causing the first action associated with the first node to be performed.

16. The one or more non-transitory computer-readable media of clause 15,wherein selecting the first node comprises:

setting a beginning node as a current node, the current node comprisinga plurality of child nodes;

computing a score for each child node of the plurality of child nodes togenerate a plurality of scores;

selecting a first child node of the plurality of child nodes using theplurality of scores; and

setting the first child node as the current node.

17. The one or more non-transitory computer-readable media of clause 15,wherein computing the plurality of features comprises encoding words ofthe text and processing the encoded words with a neural network.

18. The one or more non-transitory computer-readable media of clause 15,wherein the first classifier comprises a logistic regression classifier.

19. The one or more non-transitory computer-readable media of clause 15,wherein selecting the first node using the first score and the secondscore comprises:

comparing the first score to a first threshold; and

comparing the second score to a second threshold, wherein the secondthreshold is not equal to the first threshold.

20. The one or more non-transitory computer-readable media of clause 15,wherein selecting the first node comprises using information about theuser.

Generation of Graphs and Classifiers

In the above description, an action graph, node selector classifiers,and action selector classifiers were used to perform semantic processingof a customer request and provide a response to a customer. The actiongraph and classifiers need to be created or trained before they can beused to perform semantic processing and techniques for creating anaction graph and classifiers are now described.

In some implementations, an action graph, such as the action graph ofFIG. 4, may be created manually. A person familiar with customerrequests for a company may be able to identify common subjects ofcustomer requests, create nodes for different categories of requests,and connect related nodes together.

In some implementations, an action graph may be created to mirror anexisting structure that allows customers to obtain information. Forexample, a menu hierarchy from an app or a website may be converted toan action graph where each page of the app or website may become a nodeof the action graph. Similarly, an existing hierarchy of an interactivevoice response system may be converted into an action graph.

In some implementations, existing logs of customer support sessions maybe used to automatically create an action graph. For example, a companymay have previously provided support to customers via an online chatsystem where customer support representatives would manually respond tocustomer requests. The transcripts of these chat sessions may beautomatically processed using natural language processing techniques(e.g., topic modeling) to identify the most common subject matter ofcustomer support requests, and the most common subject matters maybecome nodes of an action graph. Recordings of phone support calls couldbe used in a similar manner after converting the recordings to textusing speech recognition.

Node selector classifiers may be specified by models that are trainedusing a training corpus. Where no existing data is available fortraining node selector classifiers, a training corpus may be createdmanually by generating text that a customer may be expected to use forvarious types of requests. For example, for the action graph of FIG. 4,a training corpus could be created manually by asking 100 people whatphrases they would likely use to make requests corresponding to eachtype of node. The node selector classifiers could then be trained usingthis manually created corpus.

In some situations, existing data may be converted into a format thatmay be used as a training corpus for node selector classifiers. Forexample, where a company previously had online chat support, thetranscripts of the chat support could be used to create a trainingcorpus. Each text message written by a customer could be labeled by aperson as corresponding to a node of the action graph. The labeled datamay then be used to train node selector classifiers.

After the company has implemented a semantic response system, thecustomer requests may be saved to further improve node selectorclassifiers. The performance of the semantic response system can bemanually evaluated. Where it is determined that a node selectorclassifier selected an incorrect node, that customer request may belabeled with the correct node and added to the training corpus to latertrain better node selector classifiers.

The company may obtain a labeled training corpus for training nodeselector classifiers using any of the techniques above. With thislabeled training corpus, node selector classifiers may be created usingtechniques known to one of skill in the art. In some implementations,encoding techniques (from NLP feature extraction) may be trained jointlywith the classifiers.

In some implementations, a node selector classifier may be created foreach node independent from the other nodes in the action graph. To traina first node of the action graph, a subset of the training corpus may beextracted that relates to the first node of the action graph, and nodeselector classifier for the first node may be trained with that subsetof the corpus. For example, if the NLP features are encoded using bag ofwords or term frequency inverse document frequency, the node selectormodels may be trained by using BFGS (Broyden-Fletcher-Goldfarb-Shannoalgorithm) to minimize the cross entropy between the negative loglikelihood of the data and the training labels represented as one-hotvectors. In another example, if the NLP features are encoded using amatrix of word embeddings, the node selector models may be trained byusing stochastic gradient descent to minimize the cross entropy of theclassifier for the labeled training data. In some implementations, theentire action graph of node selector classifiers may be trained jointlyusing the entire corpus of training data.

In addition to training the node selector classifiers, a threshold mayneed to be determined for when a score of a node is high enough for thenode to be accepted as a correct choice. In some implementations, it maybe decided that the threshold should be high to minimize the probabilityof an incorrect node being selected. As described above, each nodeselector classifier may have its own threshold. To determine anappropriate threshold for a node selector classifier, a precision/recallcurve may be plotted for the node selector classifier (e.g., using aportion of the training corpus that was reserved for tuning theclassifiers), and a point on the curve may be selected according to adesired probability for an error rate, such as a probability of a falsealarm or false acceptance. Once the probability has been specified, athreshold may be determined from precision/recall curve that will allowthe node selector classifier to obtain the desired error rate inpractice.

The action selector classifiers may be trained in a similar manner asthe node selector classifiers. Training data may be created manually,may be created by labeling an existing data set (e.g., existing onlinechat transcripts), or obtained from operation of the semantic responsesystem with an initial (e.g., bootstrap) model. Once training data hasbeen obtained, the action selector classifiers may be trained usingtechniques known to one of skill in the art. In some implementations,such as when the action selector classifiers are implemented usingdecision trees, the action selector classifiers may be created manually.

Customer Service Representative Interface

The above description of providing responses to customer requests usingsemantic processing allowed for an automated response (e.g., without theinvolvement of a person). In some situations, a customer may communicatewith a CSR to obtain assistance. The customer and CSR may communicatewith each other using any combination of typing messages or by speaking(e.g., using a microphone of a customer device or in a phone call).

Where the customer and CSR are communicating by text, the messages maybe typed using any suitable user interface or transcribed using speechrecognition. For example, the customer may type a text message, anemail, into a text box on an app, or in a text box on a website. Thecustomer may communicate with the company directly (e.g., using acompany phone number, email address, app, or website) or may communicatevia a third party, such as a social networking company or a third-partyservice (discussed in greater detail below). When the customer and CSRare communicating using speech, their speech may be transmitted to eachother using any suitable interface, such as a phone call or an onlinecommunication tool (e.g., Skype or Google Hangouts).

For clarity of presentation, the following description will use textcommunication as an example, but the same techniques may also be usedwhen communicating by speech.

A CSR may be assisting multiple customers simultaneously. A company maydesire for its CSRs to be as efficient as possible in order to providethe best experience for customers and also to reduce costs. FIGS. 18A-Qprovide examples of user interfaces that may be used by a CSR and acustomer in communicating with each other. For example, FIG. 18Apresents a user interface that may be used by a CSR. FIG. 18A includes acustomer list portion 1810, that may include a list of customers thatthe CSR is currently communicating with. FIG. 18A also includesconversation portion 1820 that allows the CSR to see messages typed by acustomer, type messages to the customer, and see the conversationhistory. FIG. 18A also includes an information portion 1830 thatprovides additional information to assist the CSR, such as a customerprofile or trouble shooting information.

During a conversation between a customer and a CSR, each of the customerand the CSR may see the entire history of the conversation with messagesfrom both the customer and the CSR. For example, each messagetransmitted by the customer may appear on a display of the customerdevice and in conversation portion 1820 of the CSR user interface. Eachmessage transmitted by the CSR may also appear on the display of thecustomer device and in conversation portion 1820 of the CSR userinterface.

A CSR user interface may include various features to facilitate the CSRin responding more quickly to customers. For example, semanticprocessing techniques may be used to understanding the meaning of acustomer request and provide suggestions to the CSR. The following arethree examples of how semantic processing may be used to assist a CSR.(1) As the CSR starts typing a response to a customer, one or morepossible completions to the text may be presented on the CSR userinterface to allow the CSR to select one of the completions. Selectingan appropriate completion may make the CSR more efficient because it maytake less time to select a completion than to finishing typing amessage. For example, if the CSR has typed “how m,” a suggestedcompletion may include “how may I help you today?” The CSR way selectthe completion and not have to type the entire response. (2) A list ofpossible complete responses may be presented to the CSR where the listof possible complete responses may be generated by processing thehistory of the conversation and other information (e.g., informationabout the customer). For example, if a customer says “The MAC address is12345,” the phrase “What is the model?” may be immediately suggested tothe CSR before the CSR starts typing. (3) Resources may be automaticallysuggested to the CSR to provide information to the CSR or allow the CSRto perform actions. For example, if a customer is having a problem withan Internet connection, a trouble shooting procedure may be suggested tothe CSR, such as in information portion 1830. These three examples ofproviding suggestions and/or information to a CSR are now described.

Automatic Suggestion of Completions

As a CSR starts typing a response to a customer, one or more possiblecompletions to what the CSR is typing may be presented to allow the CSRto select one of the possible completions. The possible completions maybe updated after each character or word typed by the CSR. Afterselecting a completion, the CSR may send it to the customer as part ofthe conversation. FIG. 18I illustrates an example of a suggestedauto-completion for a text box 1822. In text box 1822, the CSR has typed“You're very welcome It's” and the suggested completion is “been mypleasure to help.”

The techniques described herein for suggesting completions are notlimited to customer support sessions and may be applied to anyapplications where automatic completions may assist a user. For example,automatic completions may be used when typing search terms into a searchengine or when an individual types a text message to a friend.

The suggested completions need not be complete sentences or phrases. Thesuggested completions may provide a suggestion for characters and/orwords to follow what the CSR has typed, but the suggested charactersand/or words may not be a complete and/or grammatically correct phraseor sentence. As used herein, a suggested completion refers to anycharacters and/or words that are suggested to follow what a CSR hastyped but they need not be grammatically correct or an entire messagethat is ready to be sent to a customer.

The auto-completion may be implemented using any appropriate techniques.In some implementations, the auto-completion may be implemented using alanguage model, such as a Knesser-Ney 5-gram language model. As the CSRtypes, sequences of likely subsequent words may be suggested. In someimplementations, the subsequent words may be determined using a beamsearch with a language model.

FIG. 9 illustrates an example of a search graph generated from a beamsearch with a language model. In the example of FIG. 9, the CSR hastyped “how m,” and the search graph has identified possible subsequentwords. In this example, the beam width is 3, and the three highestscoring paths are “how many do you . . . ”, “how may I help . . . ”, and“how much time do . . . ”, where the ellipses indicate possiblesuccessive words.

In creating the search graph, the first step may be to identify wordsthat may follow what the CSR has typed and to add paths to the searchgraph for the most likely following words. For example, bigrams from alanguage model may be used to identify the most likely words that follow“how.” In some implementations, the set of considered words may belimited to words that start with “m” because the CSR has already typed“m.” In this example, the three most likely following words are “many,”“may,” and “much,” and these are added to the search graph as activepaths.

Next, each of the active paths may be extended by identifying the mostlikely following words for each active path and retaining the top threeextended paths. For example, for each active path, trigrams from alanguage model may be used to select a next word, compute path scores,and retain the top scoring paths. In this example, the most likely pathsare “how many do,” “how may I”, and “how much time.” Similarly, thepaths may continue to be extended until a termination criteria isreached for each active path.

One or more auto-complete suggestions may then be obtained from thesearch graph and presented to the CSR. For example, the top scoringsuggestion, a top number of scoring suggestions, or all suggestions witha score above a threshold may be presented. The CSR may then select asuggestion and transmit it to the customer.

Many variations of the above example of a beam search using a languagemodel are possible, and the techniques described herein are not limitedto the above example. Any appropriate techniques known by one of skillin the art for performing auto-completion using a beam search with alanguage model may additionally be used.

The language model used for auto-completion may be general in that it isthe same for all CSRs for all conversations with all customers. Thelanguage model may also be more specific in that it is generated forparticular CSRs or categories of CSRs, customers or types of customers,or particular topics. For example, a company have different groups ofCSRs (e.g., one group may handle technical support and another group mayhandle billing), and a language model may be created for each group. Inanother example, a language model may be created for each CSR that isadapted to the communication style of that particular CSR.

In some implementations, language models may be created for differenttopics. For example, a company may identify multiple topics (e.g., thenodes or leaves of the action graph of FIG. 4) and create a languagemodel for each topic. During a conversation between a customer and aCSR, the conversation history and other information may be used toselect a topic of the support session (e.g., one of the previouslyidentified topics). A language model corresponding to the selected topicmay then be used to perform auto-completion for the CSR. In someimplementations, the topic may be updated after each communicationbetween the customer and the CSR, and where the topic changes, alanguage model for the new topic may then be used to performauto-completion for the CSR.

The language models used for auto-completion may be trained using anyappropriate techniques. The training data may be obtained from previoussupport sessions, such as all support sessions, support sessions with aparticular CSR, support sessions with high performing CSRs, or supportsessions relating to particular topics (e.g., where previous sessionsare manually annotated). The data used to train language models may bepreprocessed, such as by performing normalization or tokenization. Anyappropriate training techniques may be used for training languagemodels, such as the expectation-maximization algorithm.

In some implementations, auto-complete may be implemented using a neuralnetwork language model, such as a recurrent neural network (RNN)language model implemented with long short-term memory units. In someimplementations, a neural network language model may use otherinformation in addition to text already typed by the CSR, and this otherinformation may be used to improve the performance of the auto-complete.For example, the other information may include previous messages in thesession or a topic model. A neural network language model forimplementing auto-completion may be word based or character based, andthe following describes a character-based implementation.

FIG. 10 illustrates a system 1000 for implementing auto-completion witha neural network. In FIG. 10, current text is received. The current textmay be text typed into a text entry box, such as text typed by a CSR.

FIG. 10 includes a preprocessing component 1010 that may convert thecurrent text into a suitable form for further processing. For example, aset of allowed characters may be defined, such as a set of 70 commonkeyboard characters, and each character in the current text may bemapped to an index of the allowed set. For characters outside of theallowed set, the characters may be discarded or mapped to a specialcharacter to represent unknown characters.

FIG. 10 includes a character encoder component 1020 that may furtherprocess the current text. In some implementations, the current text maybe encoded using 1-hot vectors. For example, where 70 characters areallowed, each character may be converted into a 1-hot vector of length70, where one element of the vector is one and the other elements areall zero. The encoded current text may also include a special 1-hotvector that indicates the start of the encoded current text.

FIG. 10 includes a feature extraction component 1030 that may determinea feature vector using the encoded characters of the current text. Insome implementations, a feature vector may be computed using a neuralnetwork. For example, an RNN may be implemented as follows.

Let x_(t) represent 1-hot vectors for t from 1 to N, where N indicatesthe number of characters received (possibly including a special startvector). Let M be the hidden vector size of the RNN. The following maybe computed iteratively for t from 1 to N to obtain a feature vector:

g_(t)¹ = σ(U₁x₁ + V₁h_(t − 1) + b₁) g_(t)² = σ(U₂x₁ + V₂h_(t − 1) + b₂)g_(t)³ = σ(U₃x₁ + V₃h_(t − 1) + b₃)g_(t)⁴ = tanh (U₄x₁ + V₄h_(t − 1) + b₄)${\sigma(x)}_{i} = \frac{1}{1 + e^{- x_{i}}}$${\tanh(x)}_{i} = \frac{e^{x_{i}} - e^{- x_{i}}}{e^{x_{i}} + e^{- x_{i}}}$c_(t) = g_(t)² ⊙ c_(t − 1) + g_(t)¹ ⊙ g_(t)⁴h_(t) = g_(t)³ ⊙ tanh (c_(t))where the U_(i) are M by N matrices of parameters, V_(i) are M by Mmatrices of parameters, b_(i) are vectors of parameters of length M, ⊙is the element-wise multiplication operator, h₀ is initialized as a zerovector, and c₀ is initialized as a zero vector. After computing theabove, the vector h_(t) is a feature vector that may be used forsubsequent processing.

FIG. 10 includes a classifier component 1040 that may be used todetermine one or more characters that may follow the current text usingthe feature vector. Any appropriate classifier may be used, such as alogistic regression classifier. For example, a logistic regressionclassifier may be implemented as follows.

A logistic regression classifier may have as parameters, a matrix W anda vector b. The matrix W may have M rows and N columns and the vector bmay have length N. The classifier may compute scores using the followingequationp(c _(t) =k _(i) |c ₁ ,c ₂ , . . . ,c _(t−1))=SoftMax(Wh _(t) +b)_(i)where k_(i) represents the i^(th) character of the allowed characters.Accordingly, the classifier may determine a score (e.g., a likelihood orprobability) for each character that may follow characters that havealready been typed. For example, where the current text is “How m”, ascore may be generated for each possible subsequent character.

FIG. 10 includes a search component 1050 that may be used to identifythe highest scoring sequences of characters that may follow the currenttext. In some implementations, search component 1050 may perform asearch for high scoring sequences using a beam search as describedabove. Search component 1050 may create a search graph and add or extendpaths in the search graph using the scores received from classifiercomponent 1040.

FIG. 11 illustrates an example of a search graph that may be created bysearch component 1050. In FIG. 11, the current text is shown on theleft, and possible sequences of subsequent characters are shown. In thisexample, the beam width is 3, and the highest scoring sequences are “Howmany do you . . . ”, “How may I he . . . ”, and “How much tim . . . ”,where the ellipses indicate possible subsequent characters. At the firststep of building the search graph, the characters “a”, “o”, and “u” arethe three characters with the highest scores and added as paths to thesearch graph.

To further add subsequent characters to the search graph, processing mayreturn to feature extraction component 1030 for each character added tothe search graph (or optionally preprocessing component 1010 orcharacter encoder component 1020, where needed). Feature extractioncomponent 1030 may compute a new feature vector that takes into accountthe new character that was added to a search path. For example, wherefeature extraction component 1030 is implemented using an RNN, a newfeature vector h_(t+1) may be computed using the encoded new character,the previous feature vector h_(t), and the state of the RNN stored inc_(t).

This above process may be repeated to determine scores for successivecharacters, and as above, the search graph may be updated and thehighest scoring paths retained. In the example of FIG. 11, a first stageof processing 1110, added the nodes “a”, “o”, and “u” to the searchgraph. At a second stage of processing 1120, paths were considered forcharacters that could follow “a”, “o”, and “u”. At the second stage, thepaths “How man”, “How may”, and “How muc” had the highest scores.Accordingly, the path “How mo” was removed from the search and that pathwas discarded or marked as inactive. This process may be repeated tobuild a search graph as shown in FIG. 11 where the dashed linesindicated paths that were considered but discarded during the beamsearch.

The search process may finish when an appropriate termination criteriahas been met. For example, the search may terminate when each activepath exceeds a length threshold or reaches an end of phrase marker.After the search is completed, one or more high scoring paths throughthe search graph may then be presented as possible auto-completions to auser.

The above description of determining auto-completion suggestions usedonly the current text when determining features for performingauto-completion. In some implementations, previous messages from thesame conversation may be used to improve the auto-completion results.For example, the CSR may currently be typing a message in response tothe customer saying “I am having a problem with my Internet connection.”The content of the previous message from the customer (and otherprevious messages in the conversation) may be able to improve theresults of the auto-completion for the current text being typed by theCSR. Previous messages in the conversation will be referred to asprevious text to distinguish the current text being typed by the CSR.

In some implementations, a topic vector may be computed from theprevious text, and the topic vector may be used during the featureextraction process. FIG. 10 includes a preprocessing component 1060(which may be different from preprocessing component 1010) that maypreprocess the previous text by performing any appropriate operations,such as converting the text to lower case, removing punctuation, andperforming tokenization.

FIG. 10 includes a topic model component 1070 that may generate a topicvector from the preprocessed previous text. Topic model component 1070uses a previously-trained topic model to process the previous text andproduce a topic vector. Each element of the topic vector may be a score(e.g., a probability or a likelihood) that indicates a match between theprevious text and a topic. A topic model may be trained on an existingcorpus of customer support data using algorithms such as latent semanticindexing, latent Dirichlet allocation, or an autoencoder (such as avariational autoencoder). The topics of the topic model may begenerative in that they are deduced from the training data rather thanspecified. The topic model may be a supervised topic model or anunsupervised topic model.

Feature extraction component 1030 may receive the topic vector and usethe topic vector in performing feature extraction. In someimplementations, feature extraction component 1030 may combine theencoded text (from character encoder component 1020 or search component1050) with the topic vector, such as concatenating the two sets of datato create a longer vector. The combined vector may then be the x_(t) inthe processing described above. In some implementations the topic vectormay be combined with an encoded character vector for each iteration ofprocessing by feature extraction component 1030.

In some implementations, other information may be used by featureextraction component 1030 in computing a feature vector. For example, anidentification of the customer, an identification of the customerservice representative, or information about a customer, such as datafrom a customer profile, may be combined with an encoded charactervector when performing feature extraction.

The parameters of feature extraction component 1030 (e.g., a neuralnetwork or RNN) and classifier component 1040 (e.g., a logisticregression classifier) need to be trained using an appropriate trainingcorpus. For example, existing customer support session logs may be usedto train these parameters. For example, an RNN may be trained byminimizing the cross entropy between the negative log likelihood of thetraining corpus and encoded character input using stochastic gradientdescent. A logistic regression classifier may be trained, for example,by minimizing the cross-entropy of the model for a labeled trainingcorpus.

FIG. 12 is a flowchart of an example implementation of suggestingcompletions to a CSR. In FIG. 12, the ordering of the steps is exemplaryand other orders are possible, not all steps are required and, in someimplementations, some steps may be omitted or other steps may be added.The process of the flowcharts may be implemented, for example, by any ofthe computers or systems described herein.

At step 1210, a customer service is session is started between acustomer and a CSR, and at step 1220, current text is received. Thecurrent text may be text entered by a CSR using any appropriate inputmechanism, such as a keyboard or using speech recognition.

At step 1230, features are obtained for the current text, such as afeature vector. Any appropriate techniques may be used to obtain afeatures for the current text. In some implementations, the current textmay be preprocessed or encoded before extracting features, for example,using any of the techniques described above. In some implementations,the current text may be processed with a neural network, such as an RNN.For example, the current text may be processed iteratively where eachiteration processes a character of the current text. In someimplementations, other information may be used during the featureextraction process. For example, an encoded representation of acharacter may be combined with a topic vector describing previousmessages in the session, and this combined data may be input into aneural network at each iteration.

At step 1240, scores are determined for one or more characters that mayfollow the current text. In some implementations, a score may becomputed for each allowed character (e.g., the 70 common keyboardcharacters). Any appropriate techniques may be used to determine thescores, such as processing the feature vector from the previous stepwith a classifier. Any appropriate classifier may be used, such as alogistic regression classifier.

At step 1250, a search graph is updated. In some implementations, pathsof the search graph may be extended using the characters and scores fromthe previous step. Path scores may be computed for extended paths, andsome paths with lower path scores may be discarded. A beam searchalgorithm may be used to decide with paths to maintain and which pathsto discard.

At step 1260, it is determined if the process is complete. Anyappropriate criteria may be used to determine whether the process iscomplete. In some implementations, the process may be complete when (i)the lengths of the paths have exceeded a threshold or (ii) all the pathsof the search graph have reached a node indicating an end of a phrase.

If processing is not complete, then processing may proceed to step 1230for each active path of the search graph. Steps 1230, 1240, and 1250 maybe repeated to further extend each active path of the search graph.

If processing is complete, then processing proceeds to step 1270 whereauto-complete suggestions are provided. One or more top scoring pathsfrom the search graph may be used to determine auto-completesuggestions. For example, a top scoring path, a number of top scoringpaths, or paths with a score exceeding a threshold may be used toprovide auto-complete suggestions. The auto-complete suggestions may bepresented to the CSR using any appropriate techniques, such asdisplaying the auto-complete suggestions below a text box where the CSRis entering text.

At step 1280, a selection of an auto-complete suggestion by a CSR isreceived. For example, a CSR may click on a suggestion using a mouse ortouch a suggestion on a touch screen.

At step 1290, a message is sent to the customer using the selectedauto-complete suggestion. For example, the text typed by the CSR may becombined with the selected auto-complete suggestion and transmitted tothe customer using any appropriate messaging techniques.

In some implementations, suggested completions may be provided to a useras described in the following clauses, combinations of any two or moreof them, or in combination with other clauses presented herein.

1. A computer-implemented method for suggesting a completion to textentered by a user, the method comprising:

receiving text of a message from a first user;

generating a topic vector using the text of the message from the firstuser, wherein each element of the topic vector comprises a scorecorresponding to a topic of a plurality of topics;

causing the message to be presented to a second user;

receiving text entered by the second user;

generating a first feature vector using the topic vector and the textentered by the second user;

identifying a first plurality of characters to follow the text enteredby the second user by processing the first feature vector, wherein thefirst plurality of characters comprises a first character;

generating a second feature vector using the topic vector and the firstcharacter;

identifying a second plurality of characters to follow the firstcharacter by processing the second feature vector, wherein the secondplurality of characters comprises a second character; and

generating a suggested completion to the text entered by the seconduser, the suggested completion comprising the first character and thesecond character.

2. The computer-implemented method of clause 1, wherein generating thetopic vector comprises using text of a second message between the firstuser and the second user.

3. The computer-implemented method of clause 1, wherein generating thefirst feature vector comprises using a neural network.

4. The computer-implemented method of clause 3, wherein the neuralnetwork comprises a recurrent neural network with long short-term memoryunits.

5. The computer-implemented method of clause 1, wherein identifying thefirst plurality of characters comprises processing the first featurevector with a classifier.

6. The computer-implemented method of clause 5, wherein the classifiercomprises a logistic regression classifier.

7. The computer-implemented method of clause 1, wherein the first useris a customer of a company and the second user is a customer servicerepresentative of the company.

8. A system for suggesting a completion to text entered by a user, thesystem comprising:

at least one server computer comprising at least one processor and atleast one memory, the at least one server computer configured to:

receive text of a message from a first user;

generate a topic vector using the text of the message from the firstuser, wherein each element of the topic vector comprises a scorecorresponding to a topic of a plurality of topics;

cause the message to be presented to a second user;

receive text entered by the second user;

generate a first feature vector using the topic vector and the textentered by the second user;

identify a first plurality of characters to follow the text entered bythe second user by processing the first feature vector, wherein thefirst plurality of characters comprises a first character;

generate a second feature vector using the topic vector and the firstcharacter;

identify a second plurality of characters to follow the first characterby processing the second feature vector, wherein the second plurality ofcharacters comprises a second character; and

generate a suggested completion to the text entered by the second user,the suggested completion comprising the first character and the secondcharacter.

9. The system of clause 8, wherein the at least one server computer isconfigured to generate the first feature vector using a neural network.

10. The system of clause 8, wherein the at least one server computer isconfigured to generate the first feature vector by:

generating a sequence of 1-hot vectors using the text entered by thesecond user;

generating a sequence of input vectors by combining each of the 1-hotvectors with the topic vector; and

processing the sequence of input vectors with a neural network.

11. The system of clause 8, wherein the at least one server computer isconfigured to generate the suggested completion by creating a graph,wherein the first character corresponds to a first node of the graph andthe second character corresponds to a second node of the graph.12. The system of clause 11, wherein the at least one server computer isconfigured to generate the suggested completion by selecting thesuggested completion using a beam search algorithm and the graph.13. The system of clause 8, wherein the at least one server computer isconfigured to:

present the suggested completion to the second user;

receive a selection of the suggested completion by the second user;

transmit a message to the first user, the transmitted message comprisingthe suggested completion.

14. The system of clause 8, wherein the at least one server computer isconfigured to generate a second suggested completion to the text enteredby the second user, the second suggested completion comprising the firstcharacter and the second character.

15. One or more non-transitory computer-readable media comprisingcomputer executable instructions that, when executed, cause at least oneprocessor to perform actions comprising:

receiving text of a message from a first user;

generating a topic vector using the text of the message from the firstuser, wherein each element of the topic vector comprises a scorecorresponding to a topic of a plurality of topics;

causing the message to be presented to a second user;

receiving text entered by the second user;

generating a first feature vector using the topic vector and the textentered by the second user;

identifying a first plurality of characters to follow the text enteredby the second user by processing the first feature vector, wherein thefirst plurality of characters comprises a first character;

generating a second feature vector using the topic vector and the firstcharacter;

identifying a second plurality of characters to follow the firstcharacter by processing the second feature vector, wherein the secondplurality of characters comprises a second character; and

generating a suggested completion to the text entered by the seconduser, the suggested completion comprising the first character and thesecond character.

16. The one or more non-transitory computer-readable media of clause 15,wherein generating the topic vector comprises using an autoencoder.

17. The one or more non-transitory computer-readable media of clause 15,wherein generating the first feature vector comprises using a neuralnetwork.

18. The one or more non-transitory computer-readable media of clause 17,wherein the neural network comprises a recurrent neural network.

19. The one or more non-transitory computer-readable media of clause 15,wherein identifying the first plurality of characters comprisesprocessing the first feature vector with a classifier.

20. The one or more non-transitory computer-readable media of clause 19,wherein the classifier comprises a logistic regression classifier.

Automatic Suggestion of Responses

When a CSR receives a message from a customer, complete responses may bepresented as suggestions to the CSR as possible responses. Where one ofthe responses is appropriate, the CSR may simply select the responseinstead of typing it. For example, where a customer types “My Internetconnection is not working,” a suggested response may include “I'm sorryto hear that. Let me help you with that.” The suggested responses may bedetermined using the message received from the customer, other previousmessages in the conversation, and/or any other relevant information.

The techniques described herein for suggesting responses are not limitedto customer support sessions and may be applied to any applicationswhere response suggestions may assist a user. For example, suggestedresponses may be used when an individual types a text message to afriend.

FIG. 18D illustrates an example of a suggested response 1821. In FIG.18D the suggested response is presented next to a text box where the CSRmay type a response. Instead of typing a response, the CSR may selectthe suggested response. Selecting the suggested response may send it tothe customer or may copy it to the text box to allow the CSR to reviewand/or edit before sending.

The automatic suggestion of responses may be implemented using anyappropriate techniques. In some implementations, suggestions forresponses may be determined by using conversation features that describeprevious messages in the conversation and response features toiteratively generate the words of a suggested response.

FIG. 13 illustrates a system 1300 for implementing auto-suggestion ofresponses. In FIG. 13, previous text of a communications session isreceived. For example, the previous text may correspond to one or moremessages between a customer and a CSR.

FIG. 13 includes a preprocessing component 1310 that may preprocess theprevious text by performing any appropriate operations, such asconverting the text to lower case, removing punctuation, and performingtokenization.

FIG. 13 includes a word encoder component 1320 that further processesthe preprocessed previous text to generate a vectorized representationof the previous text. For example, each word of the previous text may berepresented as a vector and the vectors for the words may be combined torepresent the previous text as a matrix, which may be referred to as aword matrix. In some implementations, a neural network may be used tocreate the word matrix from the previous text. In some implementations,for example, each vectorized word may have a length of 50 to 500elements. Accordingly, if there are N words in the previous text, theword matrix output by word encoder component 1320 may have N columns and50 to 500 rows (or vice versa).

FIG. 13 includes a feature encoder component 1330 that encodes the wordmatrix received from word encoder component 1320 into a conversationfeature vector. The conversation feature vector may be any featurevector that describes or relates to the meaning of the messages in theconversation. In some implementations, feature encoder component 1330may use a neural network, such as an RNN or an RNN with long short-termmemory units, as described above. Feature encoder component 1330 mayiteratively process each word of the previous text by iterativelyprocessing each column of the word matrix. For example, for a firstiteration of an RNN, x₁ (in the RNN equations above) may be the firstcolumn of the word matrix, for a second iteration, x₂ may be the secondcolumn of the word matrix and so forth. After processing all of thewords of the previous text, feature encoder component 1330 may output aconversation feature vector that may be denoted as h_(N)^(conversation).

The conservation feature vector output by feature encoder component 1330represents the previous text. This conservation feature vector may thenbe input into feature decoder component 1340 to generate suggestedresponses.

Feature decoder component 1340 may also use a neural network to decodethe conversation feature vector into a response feature vector that maybe used to generate words for suggested responses. In someimplementations, the neural network may be an RNN or an RNN with longshort-term memory units, as described above. Feature decoder component1340 may iteratively process input feature vectors (e.g., a conversationfeature vector or a response feature vector) and output a responsefeature vector at each iteration.

Feature decoder component 1340 may be initialized using informationobtained from the final iteration of feature encoder component 1330. Forexample, where feature decoder component 1340 is implemented with anRNN, the initial response feature vector, denoted as h₀ ^(response), maybe set to the value of h_(N) ^(conversation), and c₀ may be initializedto c_(N) from the last iteration of feature encoder component 1330. Atthe first iteration of feature decoder component 1340, the input to theRNN, x₀, may be a special vector indicating the beginning of a phrase.The RNN may output a response feature vector that may be referred to ash₁ ^(response).

FIG. 13 includes classifier component 1350 that may process a responsefeature vector received from feature decoder component 1340 anddetermine one or more words for a suggested response. For example, thefirst iteration of classifier component 1350 may determine one or morewords that may start a suggested response. Classifier component 1350 mayuse any appropriate classifier, such as a logistic regressionclassifier. For example, a logistic regression classifier may determinea score for each word of an allowed set of words, and a number of topscoring words may be selected as words that may be used for a suggestedresponse.

FIG. 13 includes a search component 1360 that may be used to identifyhigh scoring sequences of words that may be used for a suggestedresponse. In some implementations, search component 1360 may perform asearch for high scoring sequences words using a beam search as describedabove. Search component 1360 may create a search graph and add or extendpaths in the search graph using the scores received from classifiercomponent 1350.

FIG. 14 illustrates an example of a search graph that may be created bysearch component 1360. In FIG. 14, the special beginning token “>go<” isshown on the left, and possible sequences of subsequent words are shown.In this example, the beam width is 3, and the highest scoring sequencesare “what is the make . . . ”, “what is the model . . . ”, and “what isthe serial . . . ”, where the ellipses indicate possible subsequentwords. At the first step of building the search graph, the words “is”,“what”, and “where” are the three words with the highest scores andadded as paths to the search graph.

To further add subsequent words to the search graph, processing mayreturn to feature decoder component 1340 for each word added to thesearch graph (the new words may be encoded before returning to featuredecoder component 1340). Feature decoder component 1340 may compute anew response feature vector that takes into account the new word thatwas added to a search path. For example, where feature extractioncomponent 1340 is implemented using an RNN, a new response featurevector h_(t+1) ^(response) may be computed using the encoded new word,the previous response feature vector h_(t) ^(response) and the state ofthe RNN stored in c_(t).

This above process may be repeated to determine scores for successivewords, and as above, the search graph may be updated and the highestscoring paths retained. In the example of FIG. 14, a first stage ofprocessing 1410, added the nodes “is”, “what”, and “where” to the searchgraph. At a second stage of processing 1420, paths were considered forwords that could follow “is”, “what”, and “where”. At the second stage,the paths “what the”, “what is”, and “what are” had the highest scores.Accordingly, the paths “is” and “where” were removed from the search andthose paths were discarded or marked as inactive. This process may berepeated to build a search graph as shown in FIG. 14 where the dashedlines indicated paths that were considered but discarded during the beamsearch.

The search process may finish when an appropriate termination criteriahas been met. For example, the search may terminate when each activepath reaches an end of phrase marker. After the search is completed, oneor more high scoring paths through the search graph may then bepresented as possible suggested responses to a user.

In some implementations, other information may be used to improve thesuggested responses. The other information may include any of theinformation described above, such as an identifier of the customer, anidentifier of the CSR, or other information about the customer (e.g., asobtained from a customer profile). The other information may be used bycomponents of the system 1300, such one or both of feature encodercomponent 1330 and feature decoder component 1340. The other informationmay be combined with other input to the components. For feature encodercomponent 1330, the other information may be appended to one or morevectorized words output by word encoder component 1320. For featuredecoder component 1340, the other information may be appending to one ormore of the conversation feature vector or the response feature vectorsthat are processed by feature decoder component 1340.

In some implementations, the suggested responses may include tokens thatindicate types of information to be inserted. For example, possibletokens may indicate the name, gender, address, email address, or phonenumber of the customer. These tokens may be indicated using specialsymbols, such as “>name<” for the customer's name. Where a suggestedresponse includes such a token, a post-processing operation may beperformed to replace the token with the corresponding information aboutthe customer. For example, a token “>name<” may be replaced with thecustomer's name before suggesting the response to the CSR.

The parameters of feature encoder component 1330 (e.g., a neural networkor RNN), feature decoder component 1340 (e.g., another neural network),and classifier component 1350 (e.g., a logistic regression classifier)need to be trained using an appropriate training corpus. For example,existing customer support session logs may be used to train theseparameters. For example, an RNN and/or a logistic regression classifiermay be trained by minimizing the cross entropy between the negative loglikelihood of the training corpus and encoded word input usingstochastic gradient descent.

FIG. 15 is a flowchart of an example implementation of suggestingresponses to a CSR. In FIG. 15, the ordering of the steps is exemplaryand other orders are possible, not all steps are required and, in someimplementations, some steps may be omitted or other steps may be added.The process of the flowcharts may be implemented, for example, by any ofthe computers or systems described herein.

At step 1510, a customer service is session is started between acustomer and a CSR, and at step 1520, previous text is received. Theprevious text may include any previous text sent by a customer or a CSR.In some implementations, the previous text may include, for example, allmessages in the current conversation between the customer and the CRS ora number of most recent messages between them. The text may be enteredusing any appropriate input mechanism, such as a keyboard or usingspeech recognition.

At step 1530, conversation features are obtained for the previous text,such as a conversation feature vector. Any appropriate techniques may beused to obtain conversation features for the previous text. In someimplementations, conversation features may be obtained by preprocessingthe previous text, encoding the words of the previous text, and thenobtaining conversation features from the encoded words. In someimplementations, a neural network, such as an RNN, may be used togenerate conversation features. For example, the previous text may beprocessed iteratively where each iteration processes a word of theprevious text. In some implementations, other information may be used togenerate conversation features. For example, information about thecustomer may be appended to an encoded word vector before processing theencoded word vector with a neural network.

At step 1540, response features are obtained, such as a vector ofresponse features. For a first iteration of step 1540, response featuresmay be obtained from the conversation features of step 1530. For lateriterations of step 1540, response features may be obtained from theresponse features from a previous iteration. Any appropriate techniquesmay be used to obtain response features. In some implementations, aneural network, such as an RNN, may be used to generate responsefeatures. For example, a first iteration may generate response featuresby processing a special token indicating the beginning of a phrase andthe conversation features with a neural network. Later iterations, maygenerate response features by processing a previously generated word andresponse features from a previous iteration. In some implementations,other information may be used to generate response features. Forexample, information about the customer may be appended to responsefeatures before processing them with a neural network.

At step 1550, scores are determined for one or more words that may starta phrase (at a first iteration) or follow a previously generated words(for iterations after the first). In some implementations, a score maybe computed for each allowed word (e.g., all the words in a known wordsdictionary). Any appropriate techniques may be used to determine thescores, such as processing the response features with a classifier. Anyappropriate classifier may be used, such as a logistic regressionclassifier.

At step 1560, a search graph is updated. In some implementations, pathsof the search graph may be extended using the words and scores from theprevious step. Path scores may be computed for extended paths, and somepaths with lower path scores may be discarded. A beam search algorithmmay be used to decide with paths to maintain and which paths to discard.

At step 1570, it is determined if the process is complete. Anyappropriate criteria may be used to determine whether the process iscomplete. In some implementations, the process may be complete when allthe paths of the search graph have reached a node indicating an end of aphrase.

If processing is not complete, then processing may proceed to step 1540for each active path of the search graph. Steps 1540, 1550, and 1560 maybe repeated to further extend each active path of the search graph.

If processing is complete, then processing proceeds to step 1580 wheresuggested responses are provided. One or more top scoring paths from thesearch graph may be used to determine suggested responses. For example,a top scoring path, a number of top scoring paths, or paths with a scoreexceeding a threshold may be used to provide suggested responses. Thesuggested responses may be presented to the CSR using any appropriatetechniques, such as displaying the suggested responses below a text boxwhere the CSR may enter text.

At step 1590, a selection of a suggested response by a CSR is received.For example, a CSR may click on a suggested response using a mouse ortouch a suggestion on a touch screen.

At step 1595, a message is sent to the customer using the selectedsuggested response, and the message may be sent using any appropriatemessaging techniques.

In some implementations, suggested responses may be provided to a useras described in the following clauses, combinations of any two or moreof them, or in combination with other clauses presented herein.

1. A computer-implemented method for suggesting a response to a receivedmessage, the method comprising:

receiving text of a message from a first user;

generating a conversation feature vector using a first neural networkand the text of the message from the first user;

generating a first response feature vector using the conversationfeature vector and a second neural network;

generating a first plurality of suggested words using the first responsefeature vector, wherein the first plurality of words comprises a firstsuggested word;

generating a second response feature vector using the first suggestedword, the first response feature vector, and the second neural network;

generating a second plurality of suggested words using the classifierand the second response feature vector, wherein the second plurality ofsuggested words comprises a second suggested word;

generating a suggested response to the message from the first user usingthe first suggested word and the second suggested word.

2. The computer-implemented method of clause 1, wherein generating theconversation feature vector comprises using text of a message from thesecond user to the first user.

3. The computer-implemented method of clause 1, wherein the first neuralnetwork comprises a recurrent neural network with long short-term memoryunits.

4. The computer-implemented method of clause 1, wherein generating thefirst plurality of suggested words comprises processing the firstresponse feature vector with a classifier.

5. The computer-implemented method of clause 4, wherein the classifiercomprises a multinomial logistic regression classifier.

6. The computer-implemented method of clause 1, further comprising:

causing the suggested response to be presented to the second user;

receiving a selection of the suggested response by the second user;

transmitting the suggested response to the first user.

7. The computer-implemented method of clause 1, wherein the first useris a customer of a company and the second user is a customer servicerepresentative of the company.

8. A system for suggesting a response to a received message, the systemcomprising:

at least one server computer comprising at least one processor and atleast one memory, the at least one server computer configured to:

receive text of a message from a first user;

generate a conversation feature vector using a first neural network andthe text of the message from the first user;

generate a first response feature vector using the conversation featurevector and a second neural network;

generate a first plurality of suggested words using the first responsefeature vector, wherein the first plurality of words comprises a firstsuggested word;

generate a second response feature vector using the first suggestedword, the first response feature vector, and the second neural network;

generate a second plurality of suggested words using the classifier andthe second response feature vector, wherein the second plurality ofsuggested words comprises a second suggested word;

generate a suggested response to the message from the first user usingthe first suggested word and the second suggested word.

9. The system of clause 8, wherein the first neural network comprises arecurrent neural network.

10. The system of clause 8, wherein the at least one server computer isconfigured to generate the suggested response by creating a graph,wherein the first suggested word corresponds to a first node of thegraph and the second suggested word corresponds to a second node of thegraph.11. The system of clause 10, wherein the at least one server computer isconfigured to generate the suggested response by selecting the suggestedresponse using a beam search algorithm and the graph.12. The system of clause 8, wherein the at least one server computer isconfigured to generate the first conversation vector or the firstresponse feature vector comprises using an identity of the first user,an identity of the second user, or information about the first user.13. The system of clause 8, wherein the at least one server computer isconfigured to generate the first plurality of suggested words byprocessing the first response feature vector with a classifier.14. The system of clause 13, wherein the classifier comprises amultinomial logistic regression classifier.15. One or more non-transitory computer-readable media comprisingcomputer executable instructions that, when executed, cause at least oneprocessor to perform actions comprising:

receiving text of a message from a first user;

generating a conversation feature vector using a first neural networkand the text of the message from the first user;

generating a first response feature vector using the conversationfeature vector and a second neural network;

generating a first plurality of suggested words using the first responsefeature vector, wherein the first plurality of words comprises a firstsuggested word;

generating a second response feature vector using the first suggestedword, the first response feature vector, and the second neural network;

generating a second plurality of suggested words using the classifierand the second response feature vector, wherein the second plurality ofsuggested words comprises a second suggested word;

generating a suggested response to the message from the first user usingthe first suggested word and the second suggested word.

16. The one or more non-transitory computer-readable media of clause 15,wherein generating the conversation feature vector comprises encodingthe message from the first user with a matrix of word embeddings andprocessing the encoded message with the first neural network.17. The one or more non-transitory computer-readable media of clause 15,wherein the first neural network is an encoder neural network and thesecond neural network is a decoder neural network.18. The one or more non-transitory computer-readable media of clause 15,further comprising generating a second suggested response using thefirst suggested word and the second suggested word.19. The one or more non-transitory computer-readable media of clause 15,wherein the first neural network comprises a recurrent neural network.20. The one or more non-transitory computer-readable media of clause 15,wherein generating the first plurality of suggested words comprisesprocessing the first response feature vector with a classifier.Automatic Suggestion of Resources

A CSR may need to access various types of resources when assisting acustomer. As used herein, a resource may include any information used bya CSR to assist a customer or any user interface that allows the CSR toaccess information or perform an action. Ordinarily, a CSR may have tospend a significant amount of time in navigating a user interface toobtain needed resources. The following are several examples of resourcesthat may be used by a CSR to assist a customer.

A CSR may need to use a troubleshooting tree to assist a customer with aproblem (e.g., the customer's Internet connection is not working). Insome existing implementations, a CSR may need to navigate to a userinterface that provides access to troubleshooting trees and then find adesired troubleshooting tree. Where a large number of troubleshootingtrees are available, it may be a time consuming process to find thedesired troubleshooting tree.

A CSR may need to obtain information about products and services torespond to a customer's question. For example, a customer may want toknow if a particular cable modem is compatible with the network in thecustomer's home. To find details about a particular product or service(e.g., a cable modem), the CSR may need to navigate to a user interfacethat provides information about products and services, and then find aparticular product or service. Where a large number of products orservices are available, it may again be a time consuming process.

A CSR may need to obtain information about a particular transaction,such as a purchase of an item (e.g., a movie rental) or an invoice orpayment of an invoice. Again, it may be time consuming for a CSR tonavigate a user interface to find information about a particulartransaction or to take an action regarding a particular transaction(e.g., provide a refund for a purchase item).

FIGS. 18B, 18G, and 18K illustrate examples of automatically suggestingresources. In FIG. 18B, the suggested resource allows the CSR to quicklyreview a transaction referenced by the customer in a message. In FIG.18G, the suggested resource allows the CSR to send the customer apayment request. In FIG. 18K, the suggested resource allows the CSR toaccess a trouble shooting tree to solve the issue stated by thecustomer.

Semantic processing of messages may be used to automatically suggestresources to a CSR. After each message between a customer and a CSR, oneor more of the messages may be processed to anticipate the needs of theCSR and update the CSR user interface (or a portion of it) to suggest aresource relevant to the conversation. The resource may, for example,provide information to the CSR and/or allow the CSR to take an action.

FIG. 16 illustrates a system 1600 for suggesting a resource to a CSR.FIG. 16 includes a resources data store 1610 that may store informationabout resources that may be presented to a CSR. Resources data store1610 may include any relevant information about resources, including butnot limited to the following: a text description of the resource, textthat may be presented for a resource, or instructions or code (e.g.,HTML or Javascript) for presenting the resource. For example, resourcesdata store 1610 may store text corresponding to the nodes of atroubleshooting tree and/or information indicating how to present thetroubleshooting tree to a CSR. In some implementations, software storedin other locations may retrieve the text of the troubleshooting tree andformat it appropriately for presentation to a CSR.

During a conversation between a CSR and a customer, the text of theconversation may relate to a resource. To identify resources that arerelevant to the conversation, features may be computed for eachresource, and these features may be compared to the text of theconversation to identify resources that are relevant to theconversation. Any appropriate features may be used for a resource, suchas a feature vector or a topic model. In some implementations, a topicvector may be computed for each resource.

In the example of FIG. 16, a preprocessing component 1620 and a topicmodel component 1070 may be used to obtain a topic vector for resources.Preprocessing component 1620 may implement any of the techniquesdescribed above for preprocessing component 1060 of FIG. 10, and mayimplement additional techniques for performing preprocessing ofresources. For example, preprocessing component 1620 may be configuredto extract information about a resource from resources data store 1610that is relevant to computing a topic vector for a resource. Topic modelcomponent 1070 may implement any of the techniques of topic modelcomponent 1070 of FIG. 10. In some implementations, topic modelcomponent 1070 may be replaced more generally with a feature vectorcomponent that computes feature vectors describing a resource.

The system of FIG. 16 may compute a topic vector for each resourcestored in resources data store 1610. In some implementations, some orall of the topic vectors may be computed in advance and the topicvectors may be stored for later use. For example, the topic vectors mayalso be stored in resources data store 1610 in association with thecorresponding resources.

The system of FIG. 16, may process the topic vectors for the resourcesto identify resources that are relevant to the current conversation. Forexample, the topic vectors may be compared to conversation featurescomputed from one or more messages between a customer and a CSR. The onemore messages between a customer and a CSR may be referred to asprevious text.

To obtain conversation features for the previous text, the previous textmay be processed using preprocessing component 1310, word encodercomponent 1320, and feature encoder component 1330. These components maybe implemented using any of the techniques described above for thecorresponding components of FIG. 13. As above, feature encoder component1330 may output a conversation feature vector that describes theprevious text and may process other information, such as anidentification of the customer, an identification of the customerservice representative, or information about a customer, such as datafrom a customer profile.

FIG. 16 includes a classifier component 1640 that may select one or moreresources that are relevant to the conversation between the customer andthe CSR. Classifier component 1640 may select one or more resources byprocessing the conversation feature vectors received from featureencoder component 1330 and topic vectors (or other feature vectors)corresponding to resources.

Classifier component 1640 may include any appropriate classifier forselecting a resource using the conversation feature vector describingthe previous text and the topic vectors describing the resources. Insome implementations, classifier component 1640 may be implemented usinga multi-layer perceptron (MLP) classifier, such as a two-layer MLP witha sigmoid output.

In some implementations, an MLP may be implemented as follows. Let x bea conversation feature vector received from feature encoder 1330 thatdescribes the previous text of the conversation. Let y be a topic vectorfor a resource. Let z be a vector that is a concatenation of x and y.Let N be a size of the MLP model. A score indicating a match between theresource and the conversation may be computed as follows:h ₁=relu(W ₁ z+b ₁)h ₂=relu(W ₂ h ₁ +b ₂)relu(x)_(i)=max(x _(i),0)s(x,y)=σ(W ₃ h ₂)where matrices W₁ and W₂ are matrices of parameters of size N by N;vectors W₃, b₁, and b₂ are vectors of parameters of size N; and σ( ) isthe sigmoid function as described above. The score s(x, y) may indicatea match between the previous text and the resource.

Using classifier component 1640, a score may be computed for eachresource, and one or more resources may be selected using the scores.For example, a top scoring resource may be selected if the score isabove a threshold, all resources with a score above a threshold may beselected, or a top scoring number of resources may be selected. In someimplementations, classifier component 1640 may also use otherinformation in generating scores, such as an identification of thecustomer, an identification of the customer service representative, orinformation about a customer.

In some implementations, other techniques may be applied to reducecomputations when selecting a resource, for example, where there are alarge number of resources. For example, the feature vectors for theresources (e.g., topic vectors) may be clustered into different clustersusing an algorithm such as k-means clustering. Selecting a resource maythen proceed in multiple steps to reduce overall computations. First, acentroid may be computed for each cluster, where the centroid representsan approximate value of the feature vectors in the cluster. Thecomputation of the cluster centroids may be performed in advance.Second, a highest scoring cluster is selecting using the classifier andthe centroids for the clusters. Third, one or more high scoringresources are selected from the selected cluster by computing scores forthe resources in the selected cluster using the feature vectorscorresponding to the resources.

In some implementations, classifier component 1640 may be implementedusing a distance, such as a cosine distance. A distance may be computedbetween the conversation feature vector for the previous text and afeature vector for each of the resources. A resource may be selectedthat is closest to the previous text according to the distance metric.In some implementations, the computations may be reduced by usingtechniques, such as locally-sensitive hashing, to select a resource thatis closest to the previous text. For example, a random projection methodmay be used to create one or more hashes that may be used to select aresource with a minimum distance to the previous text.

The one or more selected resources may then be presented to a CSR. Forexample, data corresponding to the selected resource may be retrievedfrom resources data store 1610, instructions may be created forpresenting the resource (e.g., HTML), and the resource may be presentedto a CSR. Where the resource allows a CSR to take an action, a selectionof an action by the CSR may be received and the action may be performed.

In some implementations, a selected resource may have one or moreparameters or slots that need to be filled in. For example, a resourcemay relate to viewing a transaction for the purchase of the movie, andan identifier of the movie or the name of the movie may be a slot thatneeds to be filling in before presenting the resource to the CSR. Insome implementations, the slot may be filled by processing the previoustext (e.g., using named entity recognition techniques) and/orinformation from the customer profile. For example, the name of themovie may be in the previous text or obtained from a history of thecustomer's purchases. The slot in the resource may then be filled in,and the filled in resource presented to the CSR.

The parameters of the classifier of classifier component 1640 may betrained using any appropriate techniques. For example, where theclassifier includes an MLP classifier, the MLP classifier may be trainedusing a corpus of training data and minimizing a triplet rank loss forthe corpus. The corpus of training data may include transcripts ofconversations between customers and CSRs where the conversations havebeen labeled (either manually or automatically) with resources that areappropriate to the conversations.

In some implementations, the classifier may be trained as follows. Letx₁ be a conversation feature vector (e.g., as produced by featureencoder component 1330) for a first conversation of the training corpusand let y₁ be a topic vector (e.g., as produced by topic model component1070) for a resource that has been determined to be relevant to theconversation (e.g., determined manually or automatically). Let x₂ be aconversation feature vector for another conversation that is randomlyselected from the training corpus. The parameters of the model may betrained by minimizing the triplet rank loss:l(x ₁ ,x ₂ ,y ₁)=max(1−s(x ₁ ,y ₁)+s(x ₂ ,y ₁),0)This function may be minimized using any appropriate techniques, such asstochastic gradient descent. The above process may be repeated for otherconversations in the training corpus until a desired convergence hasbeen obtained.

FIG. 17 is a flowchart of an example implementation of automaticallysuggesting a resource to a CSR. In FIG. 17, the ordering of the steps isexemplary and other orders are possible, not all steps are required and,in some implementations, some steps may be omitted or other steps may beadded. The process of the flowcharts may be implemented, for example, byany of the computers or systems described herein.

At step 1710, information about resources are obtained. The informationabout the resources may include any text that is descriptive of orrelevant to the resources. The resources may include any of theresources described above.

At step 1720, features are computed for each resource. Any appropriatefeatures may be used, such as a feature vector or topic vector computedfor each resource using text that is descriptive of or relevant to theresource. Steps 1710 and 1720 may be computed once in advance of thesubsequent steps of FIG. 17.

At step 1730, a customer service session is started, and at step 1740,previous text of the customer service session is received. These stepsmay be performed using any of the techniques described above for steps1510 and 1520 of FIG. 15.

At step 1750, conversation features are computed that describes theprevious text. Any appropriate conversation features may be used, suchas a conversation feature vector created using a neural network, such asan RNN.

At step 1760, a resource is selected using the conversation features forthe previous text and the features for the resources. For example, aclassifier, such as an MLP classifier, may compute a score for eachresource, and one or more resources may be selected using the scores.For example, a resource with a highest score may be selected if thescore is above a threshold.

At step 1770, the selected resource is presented to a CSR. For example,data corresponding to the resource may be converted into a format toallow the resource to be presented on a display. In someimplementations, HTML may be generated using data of the resource and anHTML template. The resource may be presented using any appropriatetechniques. For example, where the CSR user interface is a web page,AJAX techniques may be used to modify a portion of the user interface topresent the resource to the CSR.

In some implementations, the selected resource may present relevantinformation to the CSR, and in some implementations, the selectedresource may include an action that may be taken by the CSR. Forexample, the resource may include a selectable item, such as a button,that the CSR may click to perform an action. Where the selected resourceallows the CSR to take an action, processing may proceed to steps 1780and 1790.

At step 1780, a selection of an action by the CSR is received. Forexample, the CSR may click the selectable item on the displayedresource. At step 1790, the action is performed. For example, the actionmay correspond to issuing a refund to the customer, or asking thecustomer a question relevant to troubleshooting a problem.

In some implementations, resources may be suggested to a user asdescribed in the following clauses, combinations of any two or more ofthem, or in combination with other clauses presented herein.

1. A computer-implemented method for suggesting a resource to a seconduser in responding to a first user, the method comprising:

receiving text of a message between the first user and the second user;

generating a conversation feature vector using a neural network and thetext of message;

obtaining a first feature vector corresponding to a first resource;

generating a first score for the first resource using the conversationfeature vector and the first feature vector;

obtaining a second feature vector corresponding to a second resource;

generating a second score for the second resource using the conversationfeature vector and the second feature vector;

selecting the first resource using the first score and the second score;and

causing the first resource to be presented to the second user.

2. The computer-implemented method of clause 1, wherein the firstfeature vector comprises a topic vector, wherein each element of thetopic vector comprises a score corresponding to a topic of a pluralityof topics.

3. The computer-implemented method of clause 1, wherein the neuralnetwork comprises a recurrent neural network with long short-term memoryunits.

4. The computer-implemented method of clause 1, generating the firstscore for the first resource comprises processing the conversationfeature vector and the first feature vector with a classifier.

5. The computer-implemented method of clause 4, wherein the classifiercomprises a multi-layer perceptron neural network.

6. The computer-implemented method of clause 1, wherein the first useris a customer of a company and the second user is a customer servicerepresentative of the company.

7. The computer-implemented method of clause 1, wherein generating theconversation feature vector comprises using an identity of the firstuser, an identity of the second user, or information about the firstuser.

8. A system for suggesting a resource to a second user in responding toa first user, the system comprising:

at least one server computer comprising at least one processor and atleast one memory, the at least one server computer configured to:

receive text of a message between the first user and the second user;

generate a conversation feature vector using a neural network and thetext of message;

obtain a first feature vector corresponding to a first resource;

generate a first score for the first resource using the conversationfeature vector and the first feature vector;

obtain a second feature vector corresponding to a second resource;

generate a second score for the second resource using the conversationfeature vector and the second feature vector;

select the first resource using the first score and the second score;and

cause the first resource to be presented to the second user.

9. The system of clause 8, wherein the at least one server computer isconfigured to generate the conversation feature vector using text of aplurality of messages between the first user and the second user.

10. The system of clause 8, wherein the at least one server computer isconfigured to generate the first score for the first resource bycomputing a distance using the conversation feature vector and the firstfeature vector.

11. The system of clause 8, wherein the at least one server computer isconfigured to select the first resource using the first score and thesecond score by selecting a resource with a highest score.

12. The system of clause 8, wherein the at least one server computer isconfigured to cause the first resource to be presented to the seconduser by:

generating HTML using information about the first resource; and

inserting the HTML into a web page.

13. The system of clause 12, wherein the presentation of the firstresource includes a selectable item, and the at least one servercomputer is configured to:

receive a selection of the selectable item by the second user; and

cause a message to be sent to the first user in response to theselection of the selectable item.

14. The system of clause 8, wherein the at least one server computer isconfigured to generate the first score for the first resource using anidentity of the first user, an identity of the second user, orinformation about the first user.

15. The system of clause 8, wherein the neural network comprises arecurrent neural network.

16. One or more non-transitory computer-readable media comprisingcomputer executable instructions that, when executed, cause at least oneprocessor to perform actions comprising:

receiving text of a message between the first user and the second user;

generating a conversation feature vector using a neural network and thetext of message;

obtaining a first feature vector corresponding to a first resource;

generating a first score for the first resource using the conversationfeature vector and the first feature vector;

obtaining a second feature vector corresponding to a second resource;

generating a second score for the second resource using the conversationfeature vector and the second feature vector;

selecting the first resource using the first score and the second score;and

causing the first resource to be presented to the second user.

17. The one or more non-transitory computer-readable media of clause 16,wherein the first feature vector and the second feature vector arecomputed before receiving the text of the message between the first userand the second user.

18. The one or more non-transitory computer-readable media of clause 16,wherein the first feature vector is computed using an autoencoder.

19. The one or more non-transitory computer-readable media of clause 16,generating the first score for the first resource comprises processingthe conversation feature vector and the first feature vector with aclassifier.

20. The one or more non-transitory computer-readable media of clause 16,wherein the classifier comprises a multi-layer perceptron neuralnetwork.

Example CSR User Interface

FIGS. 18A-Q illustrate example user interfaces that may be used by a CSRand a customer and that may incorporate the techniques described above.

FIG. 18A illustrates an example user interface (UI) that may be used bya CSR to communicate with one or more customers. The UI of FIG. 18Aincludes different portions that contain different types of information.For example, FIG. 18A includes a customer list portion 1810 thatincludes a list of customers who the CSR is currently communicatingwith. In this example, the CSR is communicating with five differentcustomers, and the customer named Cathy Washington is a selected oractive customer. Because Cathy Washington is the selected or activecustomer, other portions of the UI may show other information relevantto Cathy Washington, such as sequence of messages between CathyWashington and the CSR.

FIG. 18A also includes a conversation portion 1820 that shows messagesbetween the customer and the CSR. In the example of FIG. 18A, a firstmessage to the customer reads “How can we help you?” This message may beautomatically generated for each new customer support session or may betyped by the CSR. The customer responds “I need to pay the bill to myaccount and cancel the Avengers movie that was billed to me.”

In another message, the customer is asked to provide a PIN and anaccount number. The PIN and account number may be used to allow the CSRaccess to information from the customer's account. In this example, thePIN is shown as asterisks so that the CSR does not have access to thePIN number.

FIG. 18A also includes an information portion 1830 that may presentother information relevant to the customer. In this example, informationportion 1830 has three tabs along the bottom that allows a CSR to selectbetween three different types of information. The three tabs are“knowledge base,” “customer history,” and “customer profile.” In FIG.18A, the customer history tab is selected and information portion 1830shows a history of events for the selected customer.

FIG. 18B illustrates a possible subsequent UI for the CSR. In FIG. 18B,the CSR has typed a message to the customer, and this message may beseen in conversation portion 1820. Additionally, information portion1830 now shows the tab for “customer profile” and a suggested resource1831 for “View Avengers 2” is shown near the top.

The suggested resource 1831 may be selected as described above. Becausethe customer's message stated that he wants to cancel the Avenger'smovie, a suggested resource relating to viewing that particulartransaction was selected and presented to the CSR. To identify the moviethat the customer wants to cancel, the text of the conversation may beanalyzed, such as by using named entity recognition techniques. Thepurchase history of the customer may also be used to improve the namedentity recognition. For example, the customer asked about a refund for“the Avengers movie,” but because the customer actually purchasedAvengers 2, the recognized named entity is Avengers 2. The resource maythen be combined with the identified transaction to suggest a resourceto allow the CSR to view the transaction corresponding to the purchaseof the Avengers 2 movie.

FIG. 18C illustrates a possible subsequent UI for the CSR, after the CSRhas clicked the “View” button corresponding the suggested action to viewthe Avengers 2 transaction from FIG. 18B. In FIG. 18C, informationportion 1830 now shows a list of transactions of the customer includingthe purchase of the Avengers 2 movie. In FIG. 18C, a popup menu showspossible further actions that may be performed by the CSR. The popupmenu may appear automatically as a result of the CSR selecting the Viewaction from FIG. 18B or may be a result of a further action of the CSR,such as clicking or hovering a mouse over the entry for Avengers 2. Thepopup menu includes a “Refund” button that the CSR may use to providethe customer with a refund.

FIG. 18D illustrates a possible subsequent UI for the CSR, after the CSRhas clicked the Refund button from FIG. 18C. In the conversation portion1820 of FIG. 18D, a new message is shown from the CSR to the customer toinform the customer that the requested refund has been processed. Thismessage may be automatically generated and sent to the customer inresponse to the CSR clicking the Refund button in FIG. 18C.

The bottom of conversation portion 1820 of FIG. 18D also shows asuggested response 1821 that is an example of the automatic suggestionof responses described above. The text of the conversation between thecustomer and the CSR may be processed to generate a feature vector thatdescribes the conversation. That feature may then be processed togenerate the text of a suggested response as described above. Thesuggested response here states that the customer has not set up a PINfor purchases and offers to assist the customer in setting up the PIN.

For this example, the suggested response depends on information from acustomer profile. This suggested response applies only where thecustomer has not already set up a PIN for purchases. In someimplementations, the generation of a suggested response may useadditional features incorporating information from the customer profile.For example, the feature vector processed by classifier 1350 may beaugmented with additional features, such as whether the customer has setup a PIN. By incorporating customer profile information as features insuggesting responses to a CSR, more relevant responses may be suggestedto a CSR.

For this example, suggested response 1821 also includes informationabout the customer, the customer's email address. A suggested responsemay include a special token that indicates a particular type ofinformation, and the token may be replaced by the correspondinginformation about the customer. For example, a suggested response mayinclude a token “>email address<” and in presenting the suggestedresponse to the CSR, the special token may be replaced with the actualemail address of the customer.

FIG. 18E illustrates a possible subsequent UI for the CSR, after the CSRselected the suggested response from FIG. 18D. As a result of theselection of the suggested response, the message is sent to thecustomer, and accordingly the message appears in conversation portion1820 of FIG. 18E. Further, the customer has responded that he would liketo set up a PIN.

Information portion 1830 of FIG. 18E also includes several resourcesthat a CSR may use. In some implementations, these suggested resourcesare automatically presented based on processing the text of theconversation between the customer and the CSR. In some implementations,the CSR may search for resources by typing in search bar 1832 at the topof information portion 1830. In this example, the CSR has typed “pin”.The text typed be the CSR may be used to search resources database 1610and present resources that match the search terms. Any appropriatesearch techniques may be used for performing a search, such as termfrequency inverse document frequency algorithms.

FIG. 18F illustrates a possible subsequent UI for the CSR, after the CSRselected “Instructions for PIN setup” from FIG. 18E. In informationportion 1830 of FIG. 18F, the CSR has the option of sending theinstructions to the customer in three different ways.

FIG. 18G illustrates a possible subsequent UI for a CSR, after the CSRselected to send the instructions via email from FIG. 18F. In FIG. 18G,the CSR has also sent a message to the customer to inform the customerthat the PIN setup instructions have been sent and that the refund forAvengers 2 has been processed.

Information portion 1830 of FIG. 18G also shows another suggestedresource 1833. After processing the most recent messages, the suggestedresource allows the CSR to request payment from the customer within thecustomer support session.

FIG. 18H illustrates an example UI that may be used by the customer incommunicating with the CSR. In some implementations, the UI of FIG. 18Hmay be generated by a special purpose application created by the company(e.g., a smartphone app). UI 1840 shows the UI with the most recentmessages between the customer and the CSR and a pay button that was sentto the customer in response to the CSR selecting the request paymentbutton from suggested resource 1833. In FIG. 18H, the customer mayactivate the pay button in UI 1840 (e.g., by touching it), authorizepayment in US 1841 (e.g., using a fingerprint sensor to authorizepayment), and see confirmation that payment was received in UI 1842.

FIG. 18I illustrates a possible subsequent UI for the CSR afterprocessing the customer's payment. In FIG. 18I, information portion 1830indicates that payment from the customer has been processed. Also inFIG. 18I, a message has been sent to the customer to thank the customerfor making the payment. This message may be automatically sent inresponse to receiving the payment and need not be typed by the CSR.

At the bottom of conversation portion 1820 in FIG. 18I is a text entrybox 1822 where the CSR may type messages to be sent to the customer.FIG. 18I illustrates an example of providing a suggested automaticcompletion to the text typed by the CSR. In this example, the CSR hastyped “You're very welcome! It's”. The suggested completion, “been mypleasure to help,” is presented afterwards. This suggested completionmay be determined using the techniques described above. The CSR mayselect the completion to use it and send the completed message to thecustomer.

FIG. 18J illustrates a possible subsequent UI for the CSR after the CSRhas sent a message using the suggested completion. The completed messagehas been sent to the customer as can be seen in conversation portion1820 of FIG. 18J.

The CSR may now be finished assisting this customer and may assistanother customer. The CSR may select another customer from customer listportion 1810, and FIG. 18K illustrates a possible subsequent UI for theCSR after selecting the customer Ray Jackson.

In FIG. 18K, the customer has sent the message “Starting from 2 weeksago, my internet started being really slow for no reason.” Afterprocessing the text of the conversation, a suggested resource 1834 ispresented in information portion 1830. Since the customer is havingproblems with an Internet connection, the suggested resource allows theCSR to start a trouble shooting tree to assist the customer in fixingthe problem.

FIG. 18L illustrates a possible subsequent UI for the CSR after the CSRhas selected the Start button from FIG. 18K to start using the troubleshooting tree. In FIG. 18L, the first step of the trouble shooting treeis to ask the customer if the problem is with the in-home network or aWi-Fi hotspot. The CSR may select the Send Question button frominformation portion 1830 to send this question to the customer.

In some implementations, the question may be sent to the customer astext, and the customer may respond by typing a response. In someimplementations, sending the question to the customer may cause buttons(or some other user interface element) to appear in the UI of thecustomer's device. Accordingly, the customer may be able answer thequestion by selecting the appropriate button. In FIG. 18L, theconversation portion indicates that the customer has responded byindicating that the problem is with the in-home network.

FIG. 18M illustrates a possible subsequent UI for the next step of thetroubleshooting tree. This UI may be displayed in response to the CSRselecting the In-Home Network button from information portion 1830 ofFIG. 18L. In FIG. 18M, the step of the troubleshooting tree relates toconfirming the customer's equipment, and the CSR may again send thequestion to the customer by clicking the Send Question button.Conversation portion 1820 of FIG. 18M shows that the question has beensent to the customer and that the customer has responded with a serialnumber of her equipment.

FIG. 18N illustrates a possible subsequent UI for the next step of thetroubleshooting tree. This UI may be displayed in response to the CSRselecting the Yes button from information portion 1830 of FIG. 18M. Thenext step of the troubleshooting relates to performing automated checks.Information portion 1830 of FIG. 18N includes a button to allow the CSRto send a message to the customer to inform her that the CSR will beinitiating automated checks and another button to start the automatedchecks. In FIG. 18N, the CSR has selected both buttons, and acorresponding message is sent to the customer as shown in conversationportion 1820 and the automated checks are in progress as shown by theprogress bar in information portion 1830.

FIG. 18O illustrates a possible subsequent UI for the CSR after theautomated checks have been performed. For this example, the result ofthe automated checks is that a technician needs to be sent to thecustomer's house. Messages informing the customer of the results of thechecks and that a technician is needed may be automatically sent to thecustomer and examples of such messages are shown in conversation portion1820 of FIG. 18O.

Information portion 1830 of FIG. 18O also includes a button to allow theCSR to schedule an appointment for the technician with the customer. Insome implementations, pressing this button will send a message to thecustomer with information about open slots for appointments, and thecustomer may respond by typing to indicate a desired appointment. Insome implementations, a UI may be presented to the customer tofacilitate the selection of appointment.

FIG. 18P illustrates an example UI that may be presented to a customerto allow the customer to select an appointment time for a technician. Inthis example, available dates and times for appointments are shown andthe customer may select an available appointment. After the customer hasmade a selection, the selected appointment may appear in the CSR UI asshown in the conversation portion 1820 of FIG. 18O.

FIG. 18Q illustrates a possible subsequent UI for the CSR afteradditional messages have been sent between the customer and the CSR.Here, the customer would like additional assistance with her onlineaccount. In some implementations, a company may have CSRs with differentspecialties, and thus a CSR who assists with technical support may bedifferent from a CSR who assists with online accounts. In this example,the CSR may use the transfer button 1823 to transfer the customer toanother CSR who can assist the customer with her online account.

Third-Party Semantic Processing Service

In some implementations, the above techniques and services may beprovided directly by a company to its customers to improve customerservice and reduce customer service expenses. Some companies, however,may desire to provide such services to their customers but may prefer touse a solution provided by a third party. For example, smaller companiesmay find it more cost effective to use a third party service than tobuild their own services for performing semantic processing. FIGS. 19A-Cillustrate three different architectures that may be used by a thirdparty to provide semantic processing services.

FIG. 19A illustrates a system 1901 that allows a third party to providesemantic processing services to multiple companies where the third partyis an intermediary between the customer and the company for allcommunications between the customer and the company. In FIG. 19A, threecustomers are interacting with three different companies. Customerdevice 1921 is interacting with company 1931, customer device 1922 isinteracting with company 1932, and customer device 1923 is interactingwith company 1933.

A customer may interact with the third party service using any of thecommunication methods described above. For example, a customer may beusing a website or app provided by the company, may be using a websiteor app provided by the third party, may be sending a message to an emailaddress or phone number associated with the company, or may be sending amessage to an email or phone number associated with the third party.From the perspective of the customer, it may or may not be apparent thatthe customer is interacting with the company via a third party insteadof directly with the company.

For automatic processing of requests, the request of the customer may besent to the third party instead of to the company. The third party mayreceive the customer's request, perform the processing described aboveto determine an automated response, and send the automated response backto the customer. For example, the third party may implement anapplication interface component 220 to receive the customer's requestand may implement a semantic response component 240 to determine aresponse to the customer's request.

The techniques described above for using semantic processing to respondto a customer request used one or more of an action graph, nodeclassifiers, customer data, other data, and action classifiers. In someimplementations, all needed information for responding to a customerrequest may be stored by servers controlled by the third party and thethird party may not need to request such information from a company.

In some implementations, some of the needed information for respondingto a customer request may be stored by servers of the company instead ofservers of the third party. For example, a company may not want to allowa third party to store copies of large amounts of customer data. Where acompany stores some of the information needed to respond to a customerrequest, servers of the third party may send requests for information toservers of a company. For example, a company may have an API (e.g., aREST API) that allows the third party to obtain needed information.Accordingly, FIG. 19 includes network connections between the thirdparty and each of the companies.

In some implementations, the third party may implement some or allaspects of the CSR user interface described above. For example, a CSRworking on behalf of a company may provide customer service to customerby logging in to a web site provided by the third party. After loggingin to the third party website, the CSR may see a UI similar to FIG. 18A.

The third party may relay messages between the customer and the CSR. Forexample, the third party may receive a message from a customer and causeit to be displayed in the UI used by the CSR. Similarly, the third partymay receive a message from the CSR and cause it to be displayed on a UIof the customer's device.

The third party may assist the CSR by providing one or more of thesemantic processing services described above. For example, the thirdparty may process the messages transmitted between the customer and CSR(and possibly other information as described above) and provideautomatic completions to the CSR, provide suggestions of responses tothe CSR, and automatically suggest resources to assist the CSR.

A third party may also provide semantic processing services to companieswithout communicating directly with customers. FIG. 19B illustrates asystem 1902 that allows a third party to provide semantic processingservices to multiple companies where the third party communicates withthe company but does not communicate directly in the customer. In FIG.19A, the customer devices now have network connections with thecorresponding companies but not directly with the third party.

To use the semantic processing services of the third party, a companymay issue requests to servers of the third party. For example, the thirdparty may provide an API (e.g., a REST API) via its servers to allow thecompany to use the semantic processing services.

A company may receive a request from a customer and desire to usesemantic processing to provide an automatic response. The company mayhave its servers issue a request to servers of the third party todetermine a response. In some implementations, the request from thecompany to the third party may include all information needed for thethird party to determine a response, such as the text of the request,previous messages between the customer and the company, or any of theother information described above. In some implementations, the serversof the third party may request information from servers of the companyin performing processing to determine an automatic response.

To use semantic processing to assist CSRs with automatic completion,automatic suggestion of responses, and automatic suggestion ofresources, the company servers may again send requests to servers of thethird party to perform the requested semantic processing. The requestmay include all needed information or servers of the third party mayrequest needed information from the company.

A third party may also provide semantic processing services to companiesusing a combination of the techniques described above. FIG. 19Cillustrates a system 1903 that allows a third party to provide semanticprocessing services to multiple companies where the customer devices maycommunicate directly with both the company and the third party. In FIG.19C, the customer devices now have network connections with both thecorresponding companies and the third party.

Where the customer devices are connected to both the company and thethird party, each of the two connections may be used for different kindsof requests. For example, where the customer is interacting with thecompany in a way that does not require semantic processing (e.g.,navigating a web site or an app), the customer device may use theconnection with the company. Where the customer is interacting with thecompany in a way that semantic processing may facilitate theinteraction, the connection with the third party may be used.

Any combination of the above architectures may be used. For example, forautomatic responses to customer requests, the customer device may usethe connection with the third party, but for a customer support session,the customer may use the connection with the company, and the companyserver can make semantic processing requests to the servers of the thirdparty as needed (e.g., for automatic completion, automatic suggestion ofresponses, or automatic suggestion of resources with CSRs).

FIG. 20 is a flowchart of an example implementation of a semanticprocessing service. In FIG. 20, the ordering of the steps is exemplaryand other orders are possible, not all steps are required and, in someimplementations, some steps may be omitted or other steps may be added.The process of the flowcharts may be implemented, for example, by any ofthe computers or systems described herein.

At step 2010, a request is received from a user (e.g., a customer) forassistance from an entity. As used herein, an entity may refer to anindividual, a company (e.g., a corporation or a limited liabilitycompany), or any collection of individuals (e.g., a partnership). Therequest may be received from the user, such as a device of the user ormay be received from the entity, where the entity previously receivedthe request from the user. The user may transmit the request using anyof the techniques described above (e.g., using a web page or app orsending a text message). The request may include any request that may beexpressed in natural language, such as a request for information (e.g.,when a package is being delivered) or a request for an action to beperformed (e.g., changing the user's address).

At step 2020, text of the request is obtained. For example, the text maybe extracted from the request or speech recognition may be performed toobtain text from an audio signal.

At step 2030, a request for information is transmitted to the entity.For example, the request for information may be transmitted from aserver of the third-party service provider to a server of the entitywhere the request is in the form of a REST API request. The informationrequested may include any of the information described above that may beused to provide a response using semantic processing. The request mayinclude information about the user, such as a user ID number or anauthentication token.

At step 2040, the first information is received from the entity.

At step 2050 a response to the request is generated using the text andthe information. The response may be generated using any of thetechniques described above, such as a selecting a node from an actiongraph using a node selector classifier and/or selecting an action usingan action selector classifier. The response may be formatted using anyappropriate techniques, such as in the form of text, structured data(e.g., XML or JSON), or presented as HTML.

At step 2060, the response is transmitted to the user. In someimplementations, the response may be transmitted directly to the user,such as by transmitting the response to a device of the user. In someimplementations, the response may be transmitted to the user via theentity.

In some implementations, semantic processing services may be provided asdescribed in the following clauses, combinations of any two or more ofthem, or in combination with other clauses presented herein.

1. A computer-implemented method for assisting entities in responding tousers, the method comprising:

receiving a first request from a first user, wherein the first requestseeks assistance from a first entity;

obtaining first text corresponding to the first request;

transmitting a first request for first information to a first server ofthe first entity;

receiving the first information from the first entity;

automatically generating a first response using the first text and thefirst information;

transmitting the first response to the first user;

receiving a second request from a second user, wherein the secondrequest seeks assistance from a second entity;

obtaining second text corresponding to the second request;

transmitting a second request for second information to a second serverof the second entity;

receiving the second information from the second entity;

automatically generating a second response using the second text and thesecond information; and

transmitting the second response to the second user.

2. The computer-implemented method of clause 1, wherein the firstrequest comprises information identifying the first user.

3. The computer-implemented method of clause 1, wherein the firstinformation comprises information about the first user.

4. The computer-implemented method of clause 1, wherein the first useris a customer of the first entity and the second user is a customer ofthe second entity.

5. The computer-implemented method of clause 1, wherein automaticallygenerating the first response comprises processing the first text with aneural network.

6. The computer-implemented method of clause 1, wherein automaticallygenerating the first response comprises selecting a first node from afirst action graph, and generating the second response comprisesselecting a second node from a second action graph.

7. The computer-implemented method of clause 1, wherein the firstrequest is received from a first device of the first user and the firstresponse is transmitted to the first device of the first user.

8. A system for assisting entities in responding to users, the systemcomprising:

at least one server computer comprising at least one processor and atleast one memory, the at least one server computer configured to:

receive a first request from a first user, wherein the first requestseeks assistance from a first entity;

obtain first text corresponding to the first request;

transmit a first request for first information to a first server of thefirst entity;

receive the first information from the first entity;

automatically generate a first response using the first text and thefirst information;

transmit the first response to the first user;

receive a second request from a second user, wherein the second requestseeks assistance from a second entity;

obtain second text corresponding to the second request;

transmit a second request for second information to a second server ofthe second entity;

receive the second information from the second entity;

automatically generate a second response using the second text and thesecond information; and

transmit the second response to the second user.

9. The system of clause 8, wherein the first information comprisesinformation about the first user.

10. The system of clause 8, wherein the at least one server computer isconfigured to automatically generate the first response by processingthe first text with a neural network.

11. The system of clause 8, wherein the at least one server computer isconfigured to automatically generate the first response by selecting afirst node from a first action graph, and automatically generate thesecond response by selecting a second node from a second action graph.12. The system of clause 8, wherein the at least one server computer isconfigured to automatically generate the first response by selecting afirst node from a first action graph using a first classifier.13. The system of clause 12, wherein the first classifier comprises alogistic regression classifier.14. The system of clause 8, wherein the first request is received fromthe first entity and the first response is transmitted to the firstentity.15. One or more non-transitory computer-readable media comprisingcomputer executable instructions that, when executed, cause at least oneprocessor to perform actions comprising:

receiving a first request from a first user, wherein the first requestseeks assistance from a first entity;

obtaining first text corresponding to the first request;

transmitting a first request for first information to a first server ofthe first entity;

receiving the first information from the first entity;

automatically generating a first response using the first text and thefirst information;

transmitting the first response to the first user;

receiving a second request from a second user, wherein the secondrequest seeks assistance from a second entity;

obtaining second text corresponding to the second request;

transmitting a second request for second information to a second serverof the second entity;

receiving the second information from the second entity;

automatically generating a second response using the second text and thesecond information; and

transmitting the second response to the second user.

16. The one or more non-transitory computer-readable media of clause 15,wherein the first information comprises information about the firstuser.

17. The one or more non-transitory computer-readable media of clause 15,wherein automatically generating the first response comprises processingthe first text with a neural network.

18. The one or more non-transitory computer-readable media of clause 15,wherein automatically generating the first response comprises selectinga first node from a first action graph, and generating the secondresponse comprises selecting a second node from a second action graph.19. The one or more non-transitory computer-readable media of clause 15,wherein automatically generating the first response comprises selectinga first node from a first action graph using a first classifier.20. The one or more non-transitory computer-readable media of clause 15,wherein the first classifier comprises a logistic regression classifier.

FIG. 21 illustrates components of one implementation of a computingdevice 2100 for implementing any of the techniques described above. InFIG. 21, the components are shown as being on a single computing device2100, but the components may be distributed among multiple computingdevices, such as a system of computing devices, including, for example,an end-user computing device (e.g., a smart phone or a tablet) and/or aserver computing device (e.g., cloud computing).

Computing device 2100 may include any components typical of a computingdevice, such as volatile or nonvolatile memory 2110, one or moreprocessors 2111, and one or more network interfaces 2112. Computingdevice 2100 may also include any input and output components, such asdisplays, keyboards, and touch screens. Computing device 2100 may alsoinclude a variety of components or modules providing specificfunctionality, and these components or modules may be implemented insoftware, hardware, or a combination thereof. Below, several examples ofcomponents are described for one example implementation, and otherimplementations may include additional components or exclude some of thecomponents described below.

Computing device 2100 may have a speech recognition component 2120 thatprocesses an audio signal containing speech to obtain text correspondingto the speech. Computing device 2100 may have an application interfacecomponent 2121 that may implement any processing needed to receiveinformation from other computers or to transmit information to othercomputers (e.g., load balancers, web servers, etc.). Applicationinterface component 2121 may also facilitate communications betweenother components. For example, application interface component 2121 mayreceive audio of a request, cause speech recognition to be performed,and then transmit the text to other components. Computing device 2100may have a customer support component 2123 that facilitates customersupport sessions between customers and CSRs. For example, customersupport component 2123 may provide a user interface for the customerand/or the CSR and may facilitate the exchange of messages. Computingdevice 2100 may have a semantic response component 2123 that mayfacilitate providing automatic responses to customer requests usingsemantic processing as described above. Computing device 2100 may havean auto complete component 2124 that uses semantic processing to providesuggestions for completions of text the CSRs have started typing asdescribed above. Computing device 2100 may have an auto-suggestresponses component 2125 that uses semantic processing to providesuggested responses to CSRs as described above. Computing device 2100may have an auto-suggest resources component 2126 that uses semanticprocessing to suggest resources to CSRs as described above.

Computing device 2100 may include or have access to various data stores,such as data stores 2130, 2131, 2132, and 2133. Data stores may use anyknown storage technology such as files or relational or non-relationaldatabases. For example, computing device 2100 may have an action graphsdata store 2130 to store the action graphs described above. Computingdevice 2100 may have a classifiers data store 2131 that may storeinformation about any of the classifiers described above. Computingdevice 2100 may have customer data data store 2132 that may be used tostore any relevant information about customers. Computing device 2100may have an other data data store 2133 that may be used to store anyother relevant data that may be used in performing the semanticprocessing tasks described above, such as a company knowledge base orinformation about the operation of company services (e.g., networkoutages).

The methods and systems described herein may be deployed in part or inwhole through a machine that executes computer software, program codes,and/or instructions on a processor. “Processor” as used herein is meantto include at least one processor and unless context clearly indicatesotherwise, the plural and the singular should be understood to beinterchangeable. The present invention may be implemented as a method onthe machine, as a system or apparatus as part of or in relation to themachine, or as a computer program product embodied in a computerreadable medium executing on one or more of the machines. The processormay be part of a server, client, network infrastructure, mobilecomputing platform, stationary computing platform, or other computingplatform. A processor may be any kind of computational or processingdevice capable of executing program instructions, codes, binaryinstructions and the like. The processor may be or include a signalprocessor, digital processor, embedded processor, microprocessor or anyvariant such as a co-processor (math co-processor, graphic co-processor,communication co-processor and the like) and the like that may directlyor indirectly facilitate execution of program code or programinstructions stored thereon. In addition, the processor may enableexecution of multiple programs, threads, and codes. The threads may beexecuted simultaneously to enhance the performance of the processor andto facilitate simultaneous operations of the application. By way ofimplementation, methods, program codes, program instructions and thelike described herein may be implemented in one or more thread. Thethread may spawn other threads that may have assigned prioritiesassociated with them; the processor may execute these threads based onpriority or any other order based on instructions provided in theprogram code. The processor may include memory that stores methods,codes, instructions and programs as described herein and elsewhere. Theprocessor may access a storage medium through an interface that maystore methods, codes, and instructions as described herein andelsewhere. The storage medium associated with the processor for storingmethods, programs, codes, program instructions or other type ofinstructions capable of being executed by the computing or processingdevice may include but may not be limited to one or more of a CD-ROM,DVD, memory, hard disk, flash drive, RAM, ROM, cache and the like.

A processor may include one or more cores that may enhance speed andperformance of a multiprocessor. In embodiments, the process may be adual core processor, quad core processors, other chip-levelmultiprocessor and the like that combine two or more independent cores(called a die).

The methods and systems described herein may be deployed in part or inwhole through a machine that executes computer software on a server,client, firewall, gateway, hub, router, or other such computer and/ornetworking hardware. The software program may be associated with aserver that may include a file server, print server, domain server,internet server, intranet server and other variants such as secondaryserver, host server, distributed server and the like. The server mayinclude one or more of memories, processors, computer readable media,storage media, ports (physical and virtual), communication devices, andinterfaces capable of accessing other servers, clients, machines, anddevices through a wired or a wireless medium, and the like. The methods,programs, or codes as described herein and elsewhere may be executed bythe server. In addition, other devices required for execution of methodsas described in this application may be considered as a part of theinfrastructure associated with the server.

The server may provide an interface to other devices including, withoutlimitation, clients, other servers, printers, database servers, printservers, file servers, communication servers, distributed servers andthe like. Additionally, this coupling and/or connection may facilitateremote execution of program across the network. The networking of someor all of these devices may facilitate parallel processing of a programor method at one or more location without deviating from the scope ofthe invention. In addition, any of the devices attached to the serverthrough an interface may include at least one storage medium capable ofstoring methods, programs, code and/or instructions. A centralrepository may provide program instructions to be executed on differentdevices. In this implementation, the remote repository may act as astorage medium for program code, instructions, and programs.

The software program may be associated with a client that may include afile client, print client, domain client, internet client, intranetclient and other variants such as secondary client, host client,distributed client and the like. The client may include one or more ofmemories, processors, computer readable media, storage media, ports(physical and virtual), communication devices, and interfaces capable ofaccessing other clients, servers, machines, and devices through a wiredor a wireless medium, and the like. The methods, programs, or codes asdescribed herein and elsewhere may be executed by the client. Inaddition, other devices required for execution of methods as describedin this application may be considered as a part of the infrastructureassociated with the client.

The client may provide an interface to other devices including, withoutlimitation, servers, other clients, printers, database servers, printservers, file servers, communication servers, distributed servers andthe like. Additionally, this coupling and/or connection may facilitateremote execution of program across the network. The networking of someor all of these devices may facilitate parallel processing of a programor method at one or more location without deviating from the scope ofthe invention. In addition, any of the devices attached to the clientthrough an interface may include at least one storage medium capable ofstoring methods, programs, applications, code and/or instructions. Acentral repository may provide program instructions to be executed ondifferent devices. In this implementation, the remote repository may actas a storage medium for program code, instructions, and programs.

The methods and systems described herein may be deployed in part or inwhole through network infrastructures. The network infrastructure mayinclude elements such as computing devices, servers, routers, hubs,firewalls, clients, personal computers, communication devices, routingdevices and other active and passive devices, modules and/or componentsas known in the art. The computing and/or non-computing device(s)associated with the network infrastructure may include, apart from othercomponents, a storage medium such as flash memory, buffer, stack, RAM,ROM and the like. The processes, methods, program codes, instructionsdescribed herein and elsewhere may be executed by one or more of thenetwork infrastructural elements.

The methods, program codes, and instructions described herein andelsewhere may be implemented on a cellular network having multiplecells. The cellular network may either be frequency division multipleaccess (FDMA) network or code division multiple access (CDMA) network.The cellular network may include mobile devices, cell sites, basestations, repeaters, antennas, towers, and the like. The cell networkmay be a GSM, GPRS, 3G, EVDO, mesh, or other networks types.

The methods, programs codes, and instructions described herein andelsewhere may be implemented on or through mobile devices. The mobiledevices may include navigation devices, cell phones, mobile phones,mobile personal digital assistants, laptops, palmtops, netbooks, pagers,electronic books readers, music players and the like. These devices mayinclude, apart from other components, a storage medium such as a flashmemory, buffer, RAM, ROM and one or more computing devices. Thecomputing devices associated with mobile devices may be enabled toexecute program codes, methods, and instructions stored thereon.Alternatively, the mobile devices may be configured to executeinstructions in collaboration with other devices. The mobile devices maycommunicate with base stations interfaced with servers and configured toexecute program codes. The mobile devices may communicate on apeer-to-peer network, mesh network, or other communications network. Theprogram code may be stored on the storage medium associated with theserver and executed by a computing device embedded within the server.The base station may include a computing device and a storage medium.The storage device may store program codes and instructions executed bythe computing devices associated with the base station.

The computer software, program codes, and/or instructions may be storedand/or accessed on machine readable media that may include: computercomponents, devices, and recording media that retain digital data usedfor computing for some interval of time; semiconductor storage known asrandom access memory (RAM); mass storage typically for more permanentstorage, such as optical discs, forms of magnetic storage like harddisks, tapes, drums, cards and other types; processor registers, cachememory, volatile memory, non-volatile memory; optical storage such asCD, DVD; removable media such as flash memory (e.g. USB sticks or keys),floppy disks, magnetic tape, paper tape, punch cards, standalone RAMdisks, Zip drives, removable mass storage, off-line, and the like; othercomputer memory such as dynamic memory, static memory, read/writestorage, mutable storage, read only, random access, sequential access,location addressable, file addressable, content addressable, networkattached storage, storage area network, bar codes, magnetic ink, and thelike.

The methods and systems described herein may transform physical and/oror intangible items from one state to another. The methods and systemsdescribed herein may also transform data representing physical and/orintangible items from one state to another.

The elements described and depicted herein, including in flow charts andblock diagrams throughout the figures, imply logical boundaries betweenthe elements. However, according to software or hardware engineeringpractices, the depicted elements and the functions thereof may beimplemented on machines through computer executable media having aprocessor capable of executing program instructions stored thereon as amonolithic software structure, as standalone software modules, or asmodules that employ external routines, code, services, and so forth, orany combination of these, and all such implementations may be within thescope of the present disclosure. Examples of such machines may include,but may not be limited to, personal digital assistants, laptops,personal computers, mobile phones, other handheld computing devices,medical equipment, wired or wireless communication devices, transducers,chips, calculators, satellites, tablet PCs, electronic books, gadgets,electronic devices, devices having artificial intelligence, computingdevices, networking equipments, servers, routers and the like.Furthermore, the elements depicted in the flow chart and block diagramsor any other logical component may be implemented on a machine capableof executing program instructions. Thus, while the foregoing drawingsand descriptions set forth functional aspects of the disclosed systems,no particular arrangement of software for implementing these functionalaspects should be inferred from these descriptions unless explicitlystated or otherwise clear from the context. Similarly, it will beappreciated that the various steps identified and described above may bevaried, and that the order of steps may be adapted to particularapplications of the techniques disclosed herein. All such variations andmodifications are intended to fall within the scope of this disclosure.As such, the depiction and/or description of an order for various stepsshould not be understood to require a particular order of execution forthose steps, unless required by a particular application, or explicitlystated or otherwise clear from the context.

The methods and/or processes described above, and steps thereof, may berealized in hardware, software or any combination of hardware andsoftware suitable for a particular application. The hardware may includea general-purpose computer and/or dedicated computing device or specificcomputing device or particular aspect or component of a specificcomputing device. The processes may be realized in one or moremicroprocessors, microcontrollers, embedded microcontrollers,programmable digital signal processors or other programmable device,along with internal and/or external memory. The processes may also, orinstead, be embodied in an application specific integrated circuit, aprogrammable gate array, programmable array logic, or any other deviceor combination of devices that may be configured to process electronicsignals. It will further be appreciated that one or more of theprocesses may be realized as a computer executable code capable of beingexecuted on a machine-readable medium.

The computer executable code may be created using a structuredprogramming language such as C, an object oriented programming languagesuch as C++, or any other high-level or low-level programming language(including assembly languages, hardware description languages, anddatabase programming languages and technologies) that may be stored,compiled or interpreted to run on one of the above devices, as well asheterogeneous combinations of processors, processor architectures, orcombinations of different hardware and software, or any other machinecapable of executing program instructions.

Thus, in one aspect, each method described above and combinationsthereof may be embodied in computer executable code that, when executingon one or more computing devices, performs the steps thereof. In anotheraspect, the methods may be embodied in systems that perform the stepsthereof, and may be distributed across devices in a number of ways, orall of the functionality may be integrated into a dedicated, standalonedevice or other hardware. In another aspect, the means for performingthe steps associated with the processes described above may include anyof the hardware and/or software described above. All such permutationsand combinations are intended to fall within the scope of the presentdisclosure.

While the invention has been disclosed in connection with the preferredembodiments shown and described in detail, various modifications andimprovements thereon will become readily apparent to those skilled inthe art. Accordingly, the spirit and scope of the present invention isnot to be limited by the foregoing examples, but is to be understood inthe broadest sense allowable by law.

All documents referenced herein are hereby incorporated by reference.

What is claimed is:
 1. A computer-implemented method for suggesting acompletion to text entered by a user on a computing device, the methodcomprising: receiving, at a sever, text of a message from a firstcomputing device of a first user; computing a topic vector using thetext of the message from the first computing device, wherein eachelement of the topic vector comprises a score corresponding to a topicof a plurality of topics; causing the message to be presented by asecond computing device to a second user; receiving text from the secondcomputing device entered by the second user; computing a first featurevector using the topic vector and the text entered by the second user;identifying a first plurality of characters to follow the text enteredby the second user by processing, by the server, the first featurevector, wherein the first plurality of characters comprises a firstcharacter; computing a second feature vector using the topic vector andthe first character; identifying a second plurality of characters tofollow the first character by processing, by the server, the secondfeature vector, wherein the second plurality of characters comprises asecond character; generating a suggested completion to the text enteredby the second user, the suggested completion comprising the firstcharacter and the second character, and transmitting the suggestedcompletion to the second computing device for presenting to the seconduser.
 2. The computer-implemented method of claim 1, wherein computingthe topic vector comprises using text of a second message between thefirst user and the second user.
 3. The computer-implemented method ofclaim 1, wherein computing the first feature vector comprises using aneural network.
 4. The computer-implemented method of claim 3, whereinthe neural network comprises a recurrent neural network with longshort-term memory units.
 5. The computer-implemented method of claim 1,wherein identifying the first plurality of characters comprisesprocessing the first feature vector with a classifier.
 6. Thecomputer-implemented method of claim 5, wherein the classifier comprisesa logistic regression classifier.
 7. The computer-implemented method ofclaim 1, wherein the first user is a customer of a company and thesecond user is a customer service representative of the company.
 8. Asystem for suggesting a completion to text entered by a user on acomputing device, the system comprising: at least one server computercomprising at least one processor and at least one memory, the at leastone server computer configured to: receive, at the server, text of amessage from a first computing device of a first user; compute a topicvector using the text of the message from the first computing device,wherein each element of the topic vector comprises a score correspondingto a topic of a plurality of topics; cause the message to be presentedby a second computing device to a second user; receive text from thesecond computing device entered by the second user; compute a firstfeature vector using the topic vector and the text entered by the seconduser; identify a first plurality of characters to follow the textentered by the second user by processing, by the server, the firstfeature vector, wherein the first plurality of characters comprises afirst character; compute a second feature vector using the topic vectorand the first character; identify a second plurality of characters tofollow the first character by processing, by the server, the secondfeature vector, wherein the second plurality of characters comprises asecond character; generate a suggested completion to the text entered bythe second user, the suggested completion comprising the first characterand the second character, and transmit the suggested completion to thesecond computing device for presenting to the second user.
 9. The systemof claim 8, wherein the at least one server computer is configured tocompute the first feature vector using a neural network.
 10. The systemof claim 8, wherein the at least one server computer is configured tocompute the first feature vector by: computing a sequence of 1-hotvectors using the text entered by the second user; computing a sequenceof input vectors by combining each of the 1-hot vectors with the topicvector; and processing the sequence of input vectors with a neuralnetwork.
 11. The system of claim 8, wherein the at least one servercomputer is configured to generate the suggested completion by creatinga graph, wherein the first character corresponds to a first node of thegraph and the second character corresponds to a second node of thegraph.
 12. The system of claim 11, wherein the at least one servercomputer is configured to generate the suggested completion by selectingthe suggested completion using a beam search algorithm and the graph.13. The system of claim 8, wherein the at least one server computer isconfigured to: present the suggested completion to the second user;receive a selection of the suggested completion by the second user;transmit a message to the first user, the transmitted message comprisingthe suggested completion.
 14. The system of claim 8, wherein the atleast one server computer is configured to generate a second suggestedcompletion to the text entered by the second user, the second suggestedcompletion comprising the first character and the second character. 15.One or more non-transitory computer-readable media comprising computerexecutable instructions that, when executed, cause at least oneprocessor to perform actions comprising: receiving, at a server, text ofa message from a first computing device of a first user; computing atopic vector using the text of the message from the first computingdevice, wherein each element of the topic vector comprises a scorecorresponding to a topic of a plurality of topics; causing the messageto be presented by a second computing device to a second user; receivingtext from the second computing device entered by the second user;computing a first feature vector using the topic vector and the textentered by the second user; identifying a first plurality of charactersto follow the text entered by the second user by processing, by theserver, the first feature vector, wherein the first plurality ofcharacters comprises a first character; computing a second featurevector using the topic vector and the first character; identifying asecond plurality of characters to follow the first character byprocessing, by the server, the second feature vector, wherein the secondplurality of characters comprises a second character; generating asuggested completion to the text entered by the second user, thesuggested completion comprising the first character and the secondcharacter, and transmitting the suggested completion to the secondcomputing device for presenting to the second user.
 16. The one or morenon-transitory computer-readable media of claim 15, wherein computingthe topic vector comprises using an autoencoder.
 17. The one or morenon-transitory computer-readable media of claim 15, wherein computingthe first feature vector comprises using a neural network.
 18. The oneor more non-transitory computer-readable media of claim 17, wherein theneural network comprises a recurrent neural network.
 19. The one or morenon-transitory computer-readable media of claim 15, wherein identifyingthe first plurality of characters comprises processing the first featurevector with a classifier.
 20. The one or more non-transitorycomputer-readable media of claim 19, wherein the classifier comprises alogistic regression classifier.